polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

yang-etal-2022-yiriyou

yiriyou@{SMM}4{H}`22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.7/

Yang, Huabin and Zhang, Zhongjian and Zhang, Yanru

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

23--26

The paper introduces the methodology proposed for the shared Task 2 of the Social Media Mining for Health Application (SMM4H) in 2022. Task 2 consists of two subtasks: Stance Detection and Premise Classification, named Subtask 2a and Subtask 2b, respectively. Our proposed system is based on dual-view attention neural networks and achieves an F1 score of 0.618 for Subtask 2a (0.068 more than the median) and an F1 score of 0.630 for Subtask 2b (0.017 less than the median). Further experiments show that the domain-specific pre-trained model, cross-validation, and pseudo-label techniques contribute to the improvement of system performance.

null

22,643

inproceedings

chizhikova-etal-2022-sinai

{SINAI}@{SMM}4{H}`22: Transformers for biomedical social media text mining in {S}panish

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.8/

Chizhikova, Mariia and L{\'o}pez-{\'U}beda, Pilar and D{\'i}az-Galiano, Manuel C. and Ure{\~n}a-L{\'o}pez, L. Alfonso and Mart{\'i}n-Valdivia, M. Teresa

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

27--30

This paper covers participation of the SINAI team in Tasks 5 and 10 of the Social Media Mining for Health ({\#}SSM4H) workshop at COLING-2022. These tasks focus on leveraging Twitter posts written in Spanish for healthcare research. The objective of Task 5 was to classify tweets reporting COVID-19 symptoms, while Task 10 required identifying disease mentions in Twitter posts. The presented systems explore large RoBERTa language models pre-trained on Twitter data in the case of tweet classification task and general-domain data for the disease recognition task. We also present a text pre-processing methodology implemented in both systems and describe an initial weakly-supervised fine-tuning phase alongside with a submission post-processing procedure designed for Task 10. The systems obtained 0.84 F1-score on the Task 5 and 0.77 F1-score on Task 10.

null

22,644

inproceedings

uludogan-yirmibesoglu-2022-boun

{BOUN}-{TABI}@{SMM}4{H}`22: Text-to-Text Adverse Drug Event Extraction with Data Balancing and Prompting

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.9/

Uludo{\u{gan, G{\"ok{\c{ce and Yirmibe{\c{so{\u{glu, Zeynep

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

31--34

This paper describes models developed for the Social Media Mining for Health 2022 Shared Task. We participated in two subtasks: classification of English tweets reporting adverse drug events (ADE) (Task 1a) and extraction of ADE spans in such tweets (Task 1b). We developed two separate systems based on the T5 model, viewing these tasks as sequence-to-sequence problems. To address the class imbalance, we made use of data balancing via over- and undersampling on both tasks. For the ADE extraction task, we explored prompting to further benefit from the T5 model and its formulation. Additionally, we built an ensemble model, utilizing both balanced and prompted models. The proposed models outperformed the current state-of-the-art, with an F1 score of 0.655 on ADE classification and a Partial F1 score of 0.527 on ADE extraction.

null

22,645

inproceedings

wei-etal-2022-uestcc

uestcc@{SMM}4{H}`22: {R}o{BERT}a based Adverse Drug Events Classification on Tweets

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.10/

Wei, Chunchen and Bi, Ran and Zhang, Yanru

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

35--37

This is a description of our participation in the ADE Mining in English Tweets shared task, organized by the Social Media Mining for Health SMM4H 2022 workshop. We participate in the subtask a of shared Task 1, and the paper introduces the system we developed for solving the task. The task requires classifying the given tweets by whether they mention the Adverse Drug Effects. We utilize RoBERTa model and apply several methods during training and finetuning period. We also try to improve the performance of our system by preprocessing the dataset but improve the precision only. The results of our system on test set are 0.601 in F1- score, 0.705 in precision, and 0.524 in recall.

null

22,646

inproceedings

he-etal-2022-zhegu

Zhegu@{SMM}4{H}-2022: The Pre-training Tweet {\&} Claim Matching Makes Your Prediction Better

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.11/

He, Pan and YuZe, Chen and Zhang, Yanru

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

38--41

SMM4H-2022 (CITATION) Task 2 is to detect whether containing premise in the tweets of users about COVID-19 on the social medias or their stances for the claims. In this paper, we propose \textbf{T}weet \textbf{C}laim \textbf{M}atching (\textbf{TCM}), which is a new pre-training task constructed by the tweets and claims similarly to Next Sentence Prediction (NSP). We first continue to pre-train the standard pre-trained language models on the labelled dataset and then fine-tune them for obtaining better performance. Compared with the solid baseline (CITATION), we achieve the absolute improvement of 7.9{\%} in Task 2a and obtain the SOTA results.

null

22,647

inproceedings

kapur-etal-2022-manlp

{M}a{NLP}@{SMM}4{H}`22: {BERT} for Classification of {T}witter Posts

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.12/

Kapur, Keshav and Harikrishnan, Rajitha and Singh, Sanjay

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

42--43

The reported work is our straightforward approach for the shared task {\textquotedblleft}Classification of tweets self-reporting age{\textquotedblright} organized by the {\textquotedblleft}Social Media Mining for Health Applications (SMM4H){\textquotedblright} workshop. This literature describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exact age(negative class). We made two submissions with variations in the preprocessing of text which yielded F1 scores of 0.80 and 0.81 when evaluated by the organizers.

null

22,648

inproceedings

kocaman-etal-2022-john

{J}ohn{\_}{S}now{\_}{L}abs@{SMM}4{H}`22: Social Media Mining for Health ({\#}{SMM}4{H}) with Spark {NLP}

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.13/

Kocaman, Veysel and Celik, Cabir and Gurbaz, Damla and Pirge, Gursev and Polat, Bunyamin and Saglamlar, Halil and Sarikaya, Meryem Vildan and Turer, Gokhan and Talby, David

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

44--47

Social media has become a major source of information for healthcare professionals but due to the growing volume of data in unstructured format, analyzing these resources accurately has become a challenge. In this study, we trained health related NER and classification models on different datasets published within the Social Media Mining for Health Applications ({\#}SMM4H 2022) workshop. Transformer based Bert for Token Classification and Bert for Sequence Classification algorithms as well as vanilla NER and text classification algorithms from Spark NLP library were utilized during this study without changing the underlying DL architecture. The trained models are available within a production-grade code base as part of the Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java.

null

22,649

inproceedings

jimeno-yepes-verspoor-2022-read

{READ}-{B}io{M}ed@{S}ocial{D}is{NER}: Adaptation of an Annotation System to {S}panish Tweets

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.14/

Jimeno Yepes, Antonio and Verspoor, Karin

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

48--51

We describe the work of the READ-BioMed team for the preparation of a submission to the SocialDisNER Disease Named Entity Recognition (NER) Task (Task 10) in 2022. We had developed a system for named entity recognition for identifying biomedical concepts in English MEDLINE citations and Spanish clinical text for the LivingNER 2022 challenge. Minimal adaptation of our system was required to perform named entity recognition in the Spanish tweets in the SocialDisNER task, given the availability of Spanish pre-trained language models and the SocialDisNER training data. Minor additions included treatment of emojis and entities in hashtags and Twitter account names.

null

22,650

inproceedings

rojas-etal-2022-pln

{PLN} {CMM} at {S}ocial{D}is{NER}: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.15/

Rojas, Matias and Barros, Jose and Martin, Kinan and Araneda-Hernandez, Mauricio and Dunstan, Jocelyn

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

52--54

This paper describes our approaches used to solve the SocialDisNER task, which belongs to the Social Media Mining for Health Applications (SMM4H) shared task. This task aims to identify disease mentions in tweets written in Spanish. The proposed model is an architecture based on the FLERT approach. It consists of fine-tuning a language model that creates an input representation of a sentence based on its neighboring sentences, thus obtaining the document-level context. The best result was obtained using an ensemble of six language models using the FLERT approach. The system achieved an F1 score of 0.862, significantly surpassing the average performance among competitor models of 0.680 on the test partition.

null

22,651

inproceedings

verma-etal-2022-claclab

{CL}a{CL}ab at {S}ocial{D}is{NER}: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in {S}panish Tweets

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.16/

Verma, Harsh and Bagherzadeh, Parsa and Bergler, Sabine

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

55--57

This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets. Before classifying each token, we encode each token with a transformer encoder using features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST gazetteer, among others. We obtain a strict F1 score of 0.869, with competition mean of 0.675, standard deviation of 0.245, and median of 0.761.

null

22,652

inproceedings

tonja-etal-2022-cic

{CIC} {NLP} at {SMM}4{H} 2022: a {BERT}-based approach for classification of social media forum posts

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.17/

Tonja, Atnafu Lambebo and Ojo, Olumide Ebenezer and Khan, Mohammed Arif and Meque, Abdul Gafar Manuel and Kolesnikova, Olga and Sidorov, Grigori and Gelbukh, Alexander

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

58--61

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks. We participated in 2 tasks: a) Task 4: Classification of Tweets self-reporting exact age and b) Task 9: Classification of Reddit posts self-reporting exact age. We evaluated the two( BERT and RoBERTa) transformer based models for both tasks. For Task 4 RoBERTa-Large achieved an F1 score of 0.846 on the test set and BERT-Large achieved an F1 score of 0.865 on the test set for Task 9.

null

22,653

inproceedings

lin-etal-2022-ncuee

{NCUEE}-{NLP}@{SMM}4{H}`22: Classification of Self-reported Chronic Stress on {T}witter Using Ensemble Pre-trained Transformer Models

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.18/

Lin, Tzu-Mi and Chen, Chao-Yi and Tzeng, Yu-Wen and Lee, Lung-Hao

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

62--64

This study describes our proposed system design for the SMM4H 2022 Task 8. We fine-tune the BERT, RoBERTa, ALBERT, XLNet and ELECTRA transformers and their connecting classifiers. Each transformer model is regarded as a standalone method to detect tweets that self-reported chronic stress. The final output classification result is then combined using the majority voting ensemble mechanism. Experimental results indicate that our approach achieved a best F1-score of 0.73 over the positive class.

null

22,654

inproceedings

morais-etal-2022-bioinfo

{B}io{I}nfo@{UAVR}@{SMM}4{H}`22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.19/

Morais, Edgar and Oliveira, Jos{\'e} Luis and Trifan, Alina and Fajarda, Olga

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

65--67

This paper describes BioInfo@UAVR team`s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.

null

22,655

inproceedings

cetina-garcia-santa-2022-fre

{FRE} at {S}ocial{D}is{NER}: Joint Learning of Language Models for Named Entity Recognition

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.20/

Cetina, Kendrick and Garc{\'i}a-Santa, Nuria

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

68--70

This paper describes our followed methodology for the automatic extraction of disease mentions from tweets in Spanish as part of the SocialDisNER challenge within the 2022 Social Media Mining for Health Applications (SMM4H) Shared Task. We followed a Joint Learning ensemble architecture for the fine-tuning of top performing pre-trained language models in biomedical domain for Named Entity Recognition tasks. We used text generation techniques to augment training data. During practice phase of the challenge our approach showed results of 0.87 F1-Score.

null

22,656

inproceedings

montanes-salas-etal-2022-itainnova

{ITAINNOVA} at {S}ocial{D}is{NER}: A Transformers cocktail for disease identification in social media in {S}panish

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.21/

Monta{\~n}{\'e}s-Salas, Rosa and L{\'o}pez-Bosque, Irene and Garc{\'i}a-Garc{\'e}s, Luis and del-Hoyo-Alonso, Rafael

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

71--74

ITAINNOVA participates in SocialDisNER with a hybrid system which combines Transformer-based Language Models (LMs) with a custom-built gazetteer for Approximate String Matching (ASM) and dedicated text processing techniques for the social media domain. Additionally, zero-shot classification capabilities have been explored in order to support different parts of the system. An extensive analysis on the interactions of these components has been accomplished, making the system stand out above the mean performance of all the participating teams.

null

22,657

inproceedings

lithgow-serrano-etal-2022-mattica

mattica@{SMM}4{H}`22: Leveraging sentiment for stance {\&} premise joint learning

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.22/

Lithgow-Serrano, Oscar and Cornelius, Joseph and Rinaldi, Fabio and Dolamic, Ljiljana

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

75--77

This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022. Our team (mattica) participated in detecting stances and premises in tweets about health mandates related to COVID-19 (Task 2). Our approach was based on using an in-domain Pretrained Language Model, which we fine-tuned by combining different strategies such as leveraging an additional stance detection dataset through two-stage fine-tuning, joint-learning Stance and Premise detection objectives; and ensembling the sentiment-polarity given by an off-the-shelf fine-tuned model.

null

22,658

inproceedings

lain-etal-2022-ku

{KU}{\_}{ED} at {S}ocial{D}is{NER}: Extracting Disease Mentions in Tweets Written in {S}panish

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.23/

Lain, Antoine and Yoon, Wonjin and Kim, Hyunjae and Kang, Jaewoo and Simpson, Ian

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

78--80

This paper describes our system developed for the Social Media Mining for Health (SMM4H) 2022 SocialDisNER task. We used several types of pre-trained language models, which are trained on Spanish biomedical literature or Spanish Tweets. We showed the difference in performance depending on the quality of the tokenization as well as introducing silver standard annotations when training the model. Our model obtained a strict F1 of 80.3{\%} on the test set, which is an improvement of +12.8{\%} F1 (24.6 std) over the average results across all submissions to the SocialDisNER challenge.

null

22,659

inproceedings

palmer-etal-2022-chaai

{CHAAI}@{SMM}4{H}`22: {R}o{BERT}a, {GPT}-2 and Sampling - An interesting concoction

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.24/

Palmer, Christopher and Khademi Habibabadi, Sedigheh and Javed, Muhammad and Dimaguila, Gerardo Luis and Buttery, Jim

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

81--84

This paper describes the approaches to the SMM4H 2022 Shared Tasks that were taken by our team for tasks 1 and 6. Task 6 was the {\textquotedblleft}Classification of tweets which indicate self-reported COVID-19 vaccination status (in English){\textquotedblright}. The best test F1 score was 0.82 using a CT-BERT model, which exceeded the median test F1 score of 0.77, and was close to the 0.83 F1 score of the SMM4H baseline model. Task 1 was described as the {\textquotedblleft}Classification, detection and normalization of Adverse Events (AE) mentions in tweets (in English){\textquotedblright}. We undertook task 1a, and with a RoBERTa-base model achieved an F1 Score of 0.61 on test data, which exceeded the mean test F1 for the task of 0.56.

null

22,660

inproceedings

sinha-etal-2022-iai

{IAI} @ {S}ocial{D}is{NER} : Catch me if you can! Capturing complex disease mentions in tweets

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.25/

Sinha, Aman and Holgado, Cristina Garcia and Clausel, Marianne and Constant, Matthieu

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

85--89

Biomedical NER is an active research area today. Despite the availability of state-of-the-art models for standard NER tasks, their performance degrades on biomedical data due to OOV entities and the challenges encountered in specialized domains. We use Flair-NER framework to investigate the effectiveness of various contextual and static embeddings for NER on Spanish tweets, in particular, to capture complex disease mentions.

null

22,661

inproceedings

trust-etal-2022-uccnlp

{UCCNLP}@{SMM}4{H}`22:Label distribution aware long-tailed learning with post-hoc posterior calibration applied to text classification

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.26/

Trust, Paul and Kadusabe, Provia and Zahran, Ahmed and Minghim, Rosane and Omala, Kizito

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

90--94

The paper describes our submissions for the Social Media Mining for Health (SMM4H) workshop 2022 shared tasks. We participated in 2 tasks: (1) classification of adverse drug events (ADE) mentions in english tweets (Task-1a) and (2) classification of self-reported intimate partner violence (IPV) on twitter (Task 7). We proposed an approach that uses RoBERTa (A Robustly Optimized BERT Pretraining Approach) fine-tuned with a label distribution-aware margin loss function and post-hoc posterior calibration for robust inference against class imbalance. We achieved a 4{\%} and 1 {\%} increase in performance on IPV and ADE respectively when compared with the traditional fine-tuning strategy with unweighted cross-entropy loss.

null

22,662

inproceedings

unnikrishnan-etal-2022-halelab

{H}ale{L}ab{\_}{NITK}@{SMM}4{H}`22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.27/

Unnikrishnan, Reshma and Kamath S, Sowmya and V. S., Ananthanarayana

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

95--97

This paper describes the techniques designed for detecting, extracting and normalizing adverse events from social data as part of the submission for the Shared task, Task 1-SMM4H`22. We present an adaptive learner mechanism for the foundation model to identify Adverse Drug Event (ADE) tweets. For the detected ADE tweets, a pipeline consisting of a pre-trained question-answering model followed by a fuzzy matching algorithm was leveraged for the span extraction and normalization tasks. The proposed method performed well at detecting ADE tweets, scoring an above-average F1 of 0.567 and 0.172 overlapping F1 for ADE normalization. The model`s performance for the ADE extraction task was lower, with an overlapping F1 of 0.435.

null

22,663

inproceedings

zhuang-zhang-2022-yet

Yet@{SMM}4{H}`22: Improved {BERT}-based classification models with Rdrop and {P}oly{L}oss

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.28/

Zhuang, Yan and Zhang, Yanru

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

98--102

This paper describes our approach for 11 classification tasks (Task1a, Task2a, Task2b, Task3a, Task3b, Task4, Task5, Task6, Task7, Task8 and Task9) from Social Media Mining for Health (SMM4H) 2022 Shared Tasks. We developed a classification model that incorporated Rdrop to augment data and avoid overfitting, Poly Loss and Focal Loss to alleviate sample imbalance, and pseudo labels to improve model performance. The results of our submissions are over or equal to the median scores in almost all tasks. In addition, our model achieved the highest score in Task4, with a higher 7.8{\%} and 5.3{\%} F1-score than the median scores in Task2b and Task3a respectively.

null

22,664

inproceedings

claeser-kent-2022-fraunhofer

Fraunhofer {FKIE} @ {SMM}4{H} 2022: System Description for Shared Tasks 2, 4 and 9

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.29/

Claeser, Daniel and Kent, Samantha

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

103--107

We present our results for the shared tasks 2, 4 and 9 at the SMM4H Workshop at COLING 2022 achieved by succesfully fine-tuning pre-trained language models to the downstream tasks. We identify the occurence of code-switching in the test data for task 2 as a possible source of considerable performance degradation on the test set scores. We successfully exploit structural linguistic similarities in the datasets of tasks 4 and 9 for training on joined datasets, scoring first in task 9 and on par with SOTA in task 4.

null

22,665

inproceedings

porvatov-semenova-2022-transformer

Transformer-based classification of premise in tweets related to {COVID}-19

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.30/

Porvatov, Vadim and Semenova, Natalia

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

108--110

Automation of social network data assessment is one of the classic challenges of natural language processing. During the COVID-19 pandemic, mining people`s stances from their public messages become crucial regarding the understanding of attitude towards health orders. In this paper, authors propose the transformer-based predictive model allowing to effectively classify presence of stance and premise in the Twitter texts.

null

22,666

inproceedings

frick-steinebach-2022-fraunhofer

Fraunhofer {SIT}@{SMM}4{H}`22: Learning to Predict Stances and Premises in Tweets related to {COVID}-19 Health Orders Using Generative Models

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.31/

Frick, Raphael and Steinebach, Martin

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

111--113

This paper describes the system used to predict stances towards health orders and to detect premises in Tweets as part of the Social Media Mining for Health 2022 (SMM4H) shared task. It takes advantage of GPT-2 to generate new labeled data samples which are used together with pre-labeled and unlabeled data to fine-tune an ensemble of GAN-BERT models. First experiments on the validation set yielded good results, although it also revealed that the proposed architecture is more suited for sentiment analysis. The system achieved a score of 0.4258 for the stance and 0.3581 for the premise detection on the test set.

null

22,667

inproceedings

khatri-etal-2022-ub

{UB} Health Miners@{SMM}4{H}`22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines.

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.32/

Khatri, Roshan and Saha, Sougata and Das, Souvik and Srihari, Rohini

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

114--117

Here we discuss our implementation of two tasks in the Social Media Mining for Health Applications (SMM4H) 2022 shared tasks {--} classification, detection, and normalization of Adverse Events (AE) mentioned in English tweets (Task 1) and classification of English tweets self-reporting exact age (Task 4). We have explored different methods and models for binary classification, multi-class classification and named entity recognition (NER) for these tasks. We have also processed the provided dataset for noise, imbalance, and creative language expression from data. Using diverse NLP methods we classified tweets for mentions of adverse drug effects (ADEs) and self-reporting the exact age in the tweets. Further, extracted reactions from the tweets and normalized these adverse effects to a standard concept ID in the MedDRA vocabulary.

null

22,668

inproceedings

sultana-etal-2022-csecu

{CSECU}-{DSG}@{SMM}4{H}`22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and {W}eb{MD} Reviews

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.33/

Sultana, Afrin and Chowdhury, Nihad Karim and Chy, Abu Nowshed

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

118--122

Medications play a vital role in medical treatment as medication non-adherence reduces clinical benefit, results in morbidity, and medication wastage. Self-declared changes in drug treatment and their reasons are automatically extracted from tweets and user reviews, helping to determine the effectiveness of drugs and improve treatment care. SMM4H 2022 Task 3 introduced a shared task focusing on the identification of non-persistent patients from tweets and WebMD reviews. In this paper, we present our participation in this task. We propose a neural approach that integrates the strengths of the transformer model, the Long Short-Term Memory (LSTM) model, and the fully connected layer into a unified architecture. Experimental results demonstrate the competitive performance of our system on test data with 61{\%} F1-score on task 3a and 86{\%} F1-score on task 3b. Our proposed neural approach ranked first in task 3b.

null

22,669

inproceedings

zohair-etal-2022-innovators

Innovators @ {SMM}4{H}`22: An Ensembles Approach for self-reporting of {COVID}-19 Vaccination Status Tweets

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.34/

Zohair, Mohammad and Bhavsar, Nidhir and Bhatnagar, Aakash and Singh, Muskaan

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

123--125

With the Surge in COVID-19, the number of social media postings related to the vaccine has grown, specifically tracing the confirmed reports by the users regarding the COVID-19 vaccine dose termed {\textquotedblleft}Vaccine Surveillance.{\textquotedblright} To mitigate this research problem, we present our novel ensembled approach for self-reporting COVID-19 vaccination status tweets into two labels, namely {\textquotedblleft}Vaccine Chatter{\textquotedblright} and {\textquotedblleft}Self Report.{\textquotedblright} We utilize state-of-the-art models, namely BERT, RoBERTa, and XLNet. Our model provides promising results with 0.77, 0.93, and 0.66 as precision, recall, and F1-score (respectively), comparable to the corresponding median scores of 0.77, 0.9, and 0.68 (respec- tively). The model gave an overall accuracy of 93.43. We also present an empirical analysis of the results to present how well the tweet was able to classify and report. We release our code base here \url{https://github.com/Zohair0209/SMM4H-2022-Task6.git}

null

22,670

inproceedings

savaliya-etal-2022-innovators

Innovators@{SMM}4{H}`22: An Ensembles Approach for Stance and Premise Classification of {COVID}-19 Health Mandates Tweets

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.35/

Savaliya, Vatsal and Bhatnagar, Aakash and Bhavsar, Nidhir and Singh, Muskaan

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

126--129

This paper presents our submission for the Shared Task-2 of classification of stance and premise in tweets about health mandates related to COVID-19 at the Social Media Mining for Health 2022. There have been a plethora of tweets about people expressing their opinions on the COVID-19 epidemic since it first emerged. The shared task emphasizes finding the level of cooperation within the mandates for their stance towards the health orders of the pandemic. Overall the shared subjects the participants to propose system`s that can efficiently perform 1) Stance Detection, which focuses on determining the author`s point of view in the text. 2) Premise Classification, which indicates whether or not the text has arguments. Through this paper we propose an orchestration of multiple transformer based encoders to derive the output for stance and premise classification. Our best model achieves a F1 score of 0.771 for Premise Classification and an aggregate macro-F1 score of 0.661 for Stance Detection. We have made our code public here

null

22,671

inproceedings

portelli-etal-2022-ailab

{AILAB}-{U}dine@{SMM}4{H}`22: Limits of Transformers and {BERT} Ensembles

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.36/

Portelli, Beatrice and Scaboro, Simone and Chersoni, Emmanuele and Santus, Enrico and Serra, Giuseppe

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

130--134

This paper describes the models developed by the AILAB-Udine team for the SMM4H`22 Shared Task. We explored the limits of Transformer based models on text classification, entity extraction and entity normalization, tackling Tasks 1, 2, 5, 6 and 10. The main takeaways we got from participating in different tasks are: the overwhelming positive effects of combining different architectures when using ensemble learning, and the great potential of generative models for term normalization.

null

22,672

inproceedings

candidato-etal-2022-air

{AIR}-{JPMC}@{SMM}4{H}`22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple {BERT}-based Models

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.37/

Candidato, Alec Louis and Gupta, Akshat and Liu, Xiaomo and Shah, Sameena

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

135--137

This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English). The goal of this task was to accurately determine if the contents of a given tweet demonstrated someone reporting their own experience with intimate partner violence. The submitted system is an ensemble of five RoBERTa models each weighted by their respective F1-scores on the validation data-set. This system performed 13{\%} better than the baseline and was the best performing system overall for this shared task.

null

22,673

inproceedings

kaur-etal-2022-arguably

{ARGUABLY}@{SMM}4{H}`22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.38/

Kaur, Prabsimran and Kohli, Guneet and Bedi, Jatin

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

138--142

With the increase in the use of social media, people have become more outspoken and are using platforms like Reddit, Facebook, and Twitter to express their views and share the medical challenges they are facing. This data is a valuable source of medical insight and is often used for healthcare research. This paper describes our participation in Task 1a, 2a, 2b, 3, 5, 6, 7, and 9 organized by SMM4H 2022. We have proposed two transformer-based approaches to handle the classification tasks. The first approach is fine-tuning single language models. The second approach is ensembling the results of BERT, RoBERTa, and ERNIE 2.0.

null

22,674

inproceedings

fu-etal-2022-casia-smm4h22

{CASIA}@{SMM}4{H}`22: A Uniform Health Information Mining System for Multilingual Social Media Texts

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.39/

Fu, Jia and Li, Sirui and Yuan, Hui Ming and Li, Zhucong and Gan, Zhen and Chen, Yubo and Liu, Kang and Zhao, Jun and Liu, Shengping

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

143--147

This paper presents a description of our system in SMM4H-2022, where we participated in task 1a,task 4, and task 6 to task 10. There are three main challenges in SMM4H-2022, namely the domain shift problem, the prediction bias due to category imbalance, and the noise in informal text. In this paper, we propose a unified framework for the classification and named entity recognition tasks to solve the challenges, and it can be applied to both English and Spanish scenarios. The results of our system are higher than the median F1-scores for 7 tasks and significantly exceed the F1-scores for 6 tasks. The experimental results demonstrate the effectiveness of our system.

null

22,675

inproceedings

guellil-etal-2022-edinburgh

{E}dinburgh{\_}{UCL}{\_}{H}ealth@{SMM}4{H}`22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.40/

Guellil, Imane and Wu, Jinge and Wu, Honghan and Sun, Tony and Alex, Beatrice

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

148--152

This paper reports on the performance of Edinburgh{\_}UCL{\_}Health`s models in the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine). Our best performing models are based on DeepADEMiner (with respective F1= 0.64, 0.62 and 0.39 for ADE identification), on a GloVe model trained on Twitter (with F1=0.11 for the change-med) and finally on a stack embedding including a layer of Glove embedding and two layers of Flair embedding (with F1= 0.77 for self-report).

null

22,676

inproceedings

francis-moens-2022-kul

{KUL}@{SMM}4{H}`22: Template Augmented Adaptive Pre-training for Tweet Classification

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.41/

Francis, Sumam and Moens, Marie-Francine

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

153--155

This paper describes models developed for the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best-performing model comprises of a template augmented task adaptive pre-training and further fine-tuning on target task data. Augmentation with random prompt templates increases the amount of task-specific data to generalize the LM to the target task domain. We explore 2 pre-training strategies: Masked language modeling (MLM) and Simple contrastive pre-training (SimSCE) and the impact of adding template augmentations with these pre-training strategies. Our system achieves an F1 score of 0.433 on the test set without using supplementary resources and medical dictionaries.

null

22,677

inproceedings

das-etal-2022-enolp

Enolp musk@{SMM}4{H}`22 : Leveraging Pre-trained Language Models for Stance And Premise Classification

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.42/

Das, Millon and Mangrulkar, Archit and Manchanda, Ishan and Kapadnis, Manav and Patnaik, Sohan

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

156--159

This paper covers our approaches for the Social Media Mining for Health (SMM4H) Shared Tasks 2a and 2b. Apart from the baseline architectures, we experiment with Parts of Speech (PoS), dependency parsing, and Tf-Idf features. Additionally, we perform contrastive pretraining on our best models using a supervised contrastive loss function. In both the tasks, we outperformed the mean and median scores and ranked first on the validation set. For stance classification, we achieved an F1-score of 0.636 using the CovidTwitterBERT model, while for premise classification, we achieved an F1-score of 0.664 using BART-base model on test dataset.

null

22,678

inproceedings

garcia-hernandez-etal-2022-air

{AIR}-{JPMC}@{SMM}4{H}`22: Identifying Self-Reported {S}panish {COVID}-19 Symptom Tweets Through Multiple-Model Ensembling

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.43/

Garcia Hernandez, Adrian and Liu, Leung Wai and Gupta, Akshat and Ravi, Vineeth and Obitayo, Saheed O. and Liu, Xiaomo and Shah, Sameena

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

160--162

We present our response to Task 5 of the Social Media Mining for Health Applications (SMM4H) 2022 competition. We share our approach into classifying whether a tweet in Spanish about COVID-19 symptoms pertain to themselves, others, or not at all. Using a combination of BERT based models, we were able to achieve results that were higher than the median result of the competition.

null

22,679

inproceedings

liu-etal-2022-air

{AIR}-{JPMC}@{SMM}4{H}`22: {BERT} + Ensembling = Too Cool: Using Multiple {BERT} Models Together for Various {COVID}-19 Tweet Identification Tasks

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.44/

Liu, Leung Wai and Gupta, Akshat and Obitayo, Saheed and Liu, Xiaomo and Shah, Sameena

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

163--167

This paper presents my submission for Tasks 1 and 2 for the Social Media Mining of Health (SMM4H) 2022 Shared Tasks competition. I first describe the background behind each of these tasks, followed by the descriptions of the various subtasks of Tasks 1 and 2, then present the methodology. Through model ensembling, this methodology was able to achieve higher results than the mean and median of the competition for the classification tasks.

null

22,680

inproceedings

karimi-flek-2022-caisa

{CAISA}@{SMM}4{H}`22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.45/

Karimi, Akbar and Flek, Lucie

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

168--170

We propose adversarial methods for increasing the robustness of disease mention detection on social media. Our method applies adversarial data augmentation on the input and the embedding spaces to the English BioBERT model. We evaluate our method in the SocialDisNER challenge at SMM4H`22 on an annotated dataset of disease mentions in Spanish tweets. We find that both methods outperform a heuristic vocabulary-based baseline by a large margin. Additionally, utilizing the English BioBERT model shows a strong performance and outperforms the data augmentation methods even when applied to the Spanish dataset, which has a large amount of data, while augmentation methods show a significant advantage in a low-data setting.

null

22,681

inproceedings

adjali-etal-2022-ofu

{OFU}@{SMM}4{H}`22: Mining Advent Drug Events Using Pretrained Language Models

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.46/

Adjali, Omar and Laleye, Fr{\'e}jus A. A. and Aggarwal, Umang

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

171--175

We describe in this paper our proposed systems for the Social Media Mining for Health 2022 shared task 1. In particular, we participated in the three sub-tasks, tasks that aim at extracting and processing Adverse Drug Events. We investigate different transformer-based pretrained models we fine-tuned on each task and proposed some improvement on the task of entity normalization.

null

22,682

inproceedings

xherija-choi-2022-complx

{C}omp{L}x@{SMM}4{H}`22: In-domain pretrained language models for detection of adverse drug reaction mentions in {E}nglish tweets

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.47/

Xherija, Orest and Choi, Hojoon

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

176--181

The paper describes the system that team CompLx developed for sub-task 1a of the Social Media Mining for Health 2022 ({\#}SMM4H) Shared Task. We finetune a RoBERTa model, a pretrained, transformer-based language model, on a provided dataset to classify English tweets for mentions of Adverse Drug Reactions (ADRs), i.e. negative side effects related to medication intake. With only a simple finetuning, our approach achieves competitive results, significantly outperforming the average score across submitted systems. We make the model checkpoints and code publicly available. We also create a web application to provide a user-friendly, readily accessible interface for anyone interested in exploring the model`s capabilities.

null

22,683

inproceedings

gasco-sanchez-etal-2022-socialdisner

The {S}ocial{D}is{NER} shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.48/

Gasco S{\'a}nchez, Luis and Estrada Zavala, Darryl and Farr{\'e}-Maduell, Eul{\`a}lia and Lima-L{\'o}pez, Salvador and Miranda-Escalada, Antonio and Krallinger, Martin

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

182--189

There is a pressing need to exploit health-related content from social media, a global source of data where key health information is posted directly by citizens, patients and other healthcare stakeholders. Use cases of disease related social media mining include disease outbreak/surveillance, mental health and pharmacovigilance. Current efforts address the exploitation of social media beyond English. The SocialDisNER task, organized as part of the SMM4H 2022 initiative, has applied the LINKAGE methodology to select and annotate a Gold Standard corpus of 9,500 tweets in Spanish enriched with disease mentions generated by patients and medical professionals. As a complementary resource for teams participating in the SocialDisNER track, we have also created a large-scale corpus of 85,000 tweets, where in addition to disease mentions, other medical entities of relevance (e.g., medications, symptoms and procedures, among others) have been automatically labelled. Using these large-scale datasets, co-mention networks or knowledge graphs were released for each entity pair type. Out of the 47 teams registered for the task, 17 teams uploaded a total of 32 runs. The top-performing team achieved a very competitive 0.891 f-score, with a system trained following a continue pre-training strategy. We anticipate that the corpus and systems resulting from the SocialDisNER track might further foster health related text mining of social media content in Spanish and inspire disease detection strategies in other languages.

null

22,684

inproceedings

pais-etal-2022-romanian

{R}omanian micro-blogging named entity recognition including health-related entities

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.49/

Pais, Vasile and Barbu Mititelu, Verginica and Irimia, Elena and Mitrofan, Maria and Gasan, Carol Luca and Micu, Roxana

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

190--196

This paper introduces a manually annotated dataset for named entity recognition (NER) in micro-blogging text for Romanian language. It contains gold annotations for 9 entity classes and expressions: persons, locations, organizations, time expressions, legal references, disorders, chemicals, medical devices and anatomical parts. Furthermore, word embeddings models computed on a larger micro-blogging corpus are made available. Finally, several NER models are trained and their performance is evaluated against the newly introduced corpus.

null

22,685

inproceedings

zanwar-etal-2022-best

The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from {R}eddit Posts

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.50/

Zanwar, Sourabh and Wiechmann, Daniel and Qiao, Yu and Kerz, Elma

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

197--202

In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data. In this paper, we aim to improve the state-of-the-art (SoTA) detection of six MHC in Reddit posts in two ways: First, we built models leveraging Bidirectional Long Short-Term Memory (BLSTM) networks trained on in-text distributions of a comprehensive set of psycholinguistic features for more explainable MHC detection as compared to black-box solutions. Second, we combine these BLSTM models with Transformers to improve the prediction accuracy over SoTA models. In addition, we uncover nuanced patterns of linguistic markers characteristic of specific MHC.

null

22,686

inproceedings

chan-etal-2022-leveraging

Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.51/

Chan, Jia-Zhen Michelle and Kunneman, Florian and Morante, Roser and L{\"osch, Lea and Zuiderent-Jerak, Teun

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

203--208

In this paper we present a procedure to extract posts that contain experiential knowledge from Facebook discussions in Dutch, using automated filtering, manual annotations and machine learning. We define guidelines to annotate experiential knowledge and test them on a subset of the data. After several rounds of (re-)annotations, we come to an inter-annotator agreement of K=0.69, which reflects the difficulty of the task. We subsequently discuss inclusion and exclusion criteria to cope with the diversity of manifestations of experiential knowledge relevant to guideline development.

null

22,687

inproceedings

adhikari-etal-2022-covid

{COVID}-19-related {N}epali Tweets Classification in a Low Resource Setting

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.52/

Adhikari, Rabin and Thapaliya, Safal and Basnet, Nirajan and Poudel, Samip and Shakya, Aman and Khanal, Bishesh

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

209--215

Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets to various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, there is still a need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models' relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at \url{https://github.com/naamiinepal/covid-tweet-classification}.

null

22,688

inproceedings

davydova-tutubalina-2022-smm4h

{SMM}4{H} 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to {COVID}-19

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.53/

Davydova, Vera and Tutubalina, Elena

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

216--220

This paper is an organizers' report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field.

null

22,689

inproceedings

weissenbacher-etal-2022-overview

Overview of the Seventh Social Media Mining for Health Applications ({\#}{SMM}4{H}) Shared Tasks at {COLING} 2022

Gonzalez-Hernandez, Graciela and Weissenbacher, Davy

oct

2022

Gyeongju, Republic of Korea

Association for Computational Linguistics

https://aclanthology.org/2022.smm4h-1.54/

Weissenbacher, Davy and Banda, Juan and Davydova, Vera and Estrada Zavala, Darryl and Gasco S{\'a}nchez, Luis and Ge, Yao and Guo, Yuting and Klein, Ari and Krallinger, Martin and Leddin, Mathias and Magge, Arjun and Rodriguez-Esteban, Raul and Sarker, Abeed and Schmidt, Lucia and Tutubalina, Elena and Gonzalez-Hernandez, Graciela

Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task

221--241

For the past seven years, the Social Media Mining for Health Applications ({\#}SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content. This seventh iteration consists of ten tasks that include English and Spanish posts on Twitter, Reddit, and WebMD. Interest in the {\#}SMM4H shared tasks continues to grow, with 117 teams that registered and 54 teams that participated in at least one task{---}a 17.5{\%} and 35{\%} increase in registration and participation, respectively, over the last iteration. This paper provides an overview of the tasks and participants' systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.

null

22,690

inproceedings

mozgai-etal-2022-toward

Toward a Semi-Automated Scoping Review of Virtual Human Smiles

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.1/

Mozgai, Sharon and Winn, Jade and Kaurloto, Cari and Leeds, Andrew and Heylen, Dirk and Hartholt, Arno

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

1--5

Smiles are a fundamental facial expression for successful human-agent communication. The growing number of publications in this domain presents an opportunity for future research and design to be informed by a scoping review of the extant literature. This semi-automated review expedites the first steps toward the mapping of Virtual Human (VH) smile research. This paper contributes an overview of the status quo of VH smile research, identifies research streams through cluster analysis, identifies prolific authors in the field, and provides evidence that a full scoping review is needed to synthesize the findings in the expanding domain of VH smile research. To enable collaboration, we provide full access to the refined VH smile dataset, key word and author word clouds, as well as interactive evidence maps.

null

22,692

inproceedings

boudin-etal-2022-smiling

Are You Smiling When {I} Am Speaking?

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.2/

Boudin, Auriane and Bertrand, Roxane and Ochs, Magalie and Blache, Philippe and Rauzy, Stephane

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

6--10

The aim of this study is to investigate conversational feedbacks that contain smiles and laughs. Firstly, we propose a statistical analysis of smiles and laughs used as generic and specific feedbacks in a corpus of French talk-in-interaction. Our results show that smiles of low intensity are preferentially used to produce generic feedbacks while high intensity smiles and laughs are preferentially used to produce specific feedbacks. Secondly, based on a machine learning approach, we propose a hierarchical classification of feedback to automatically predict not only the presence/absence of a smile but, also the type of smiles according to an intensity-scale (low or high).

null

22,693

inproceedings

hiersch-etal-2022-gender

Gender Differences, Smiling, and Economic Negotiation Outcomes

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.3/

Hiersch, Paulina and McKeown, Gary and Latu, Ioana and Rychlowska, Magdalena

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

11--15

Research documents gender differences in nonverbal behavior and negotiation outcomes. Women tend to smile more often than men and men generally perform better in economic negotiation contexts. Among nonverbal behaviors, smiling can serve various social functions, from rewarding or appeasing others to conveying dominance, and could therefore be extremely useful in economic negotiations. However, smiling has hardly been studied in negotiation contexts. Here we examine links between smiling, gender, and negotiation outcomes. We analyze a corpus of video recordings of participant dyads during mock salary negotiations and test whether women smile more than men and if the amount of smiling can predict economic negotiation outcomes. Consistent with existing literature, women smiled more than men. There was no significant relationship between smiling and negotiation outcomes and gender did not predict negotiation performance. Exploratory analyses showed that expected negotiation outcomes, strongly correlated with actual outcomes, tended to be higher for men than for women. Implications for the gender pay gap and future research are discussed.

null

22,694

inproceedings

rauzy-etal-2022-measure

A Measure of the Smiling Synchrony in the Conversational Face-to-face Interaction Corpus {PACO}-{CHEESE}

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.4/

Rauzy, St{\'e}phane and Amoyal, Mary and Priego-Valverde, B{\'e}atrice

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

16--20

The smiling synchrony of the French audio-video conversational corpora {\textquotedblleft}PACO{\textquotedblright} and {\textquotedblleft}Cheese!{\textquotedblright} is investigated. The two corpora merged altogether last 6 hours and are made of 25 face-to-face dyadic interactions annotated following the 5 levels Smiling Intensity Scale proposed by Gironzetti et al. (2016). After introducing new indicators for characterizing synchrony phenomena, we find that almost all the 25 interactions of PACO-CHEESE show a strong and significant smiling synchrony behavior. We investigate in a second step the evolution of the synchrony parameters throughout the interaction. No effect is found and it appears rather that the smiling synchrony is present at the very start of the interaction and remains unchanged throughout the conversation.

null

22,695

inproceedings

bohy-etal-2022-analysis

Analysis of Co-Laughter Gesture Relationship on {RGB} Videos in Dyadic Conversation Context

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.5/

Bohy, Hugo and Hammoudeh, Ahmad and Maiorca, Antoine and Dupont, St{\'e}phane and Dutoit, Thierry

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

21--25

The development of virtual agents has enabled human-avatar interactions to become increasingly rich and varied. Moreover, an expressive virtual agent i.e. that mimics the natural expression of emotions, enhances social interaction between a user (human) and an agent (intelligent machine). The set of non-verbal behaviors of a virtual character is, therefore, an important component in the context of human-machine interaction. Laughter is not just an audio signal, but an intrinsic relationship of multimodal non-verbal communication, in addition to audio, it includes facial expressions and body movements. Motion analysis often relies on a relevant motion capture dataset, but the main issue is that the acquisition of such a dataset is expensive and time-consuming. This work studies the relationship between laughter and body movements in dyadic conversations between two interlocutors. The body movements were extracted from videos using deep learning based pose estimator model. We found that, in the explored NDC-ME dataset, a single statistical feature (i.e, the maximum value, or the maximum of Fourier transform) of a joint movement weakly correlates with laughter intensity by 30{\%}. However, we did not find a direct correlation between audio features and body movements. We discuss about the challenges to use such dataset for the audio-driven co-laughter motion synthesis task.

null

22,696

inproceedings

wu-etal-2022-intergroup

Intergroup Bias in Smile Discrimination in Autism

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.7/

Wu, Ruihan and Hamilton, Antonia and White, Sarah

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

27

Genuine and posed smiles are important social cues (Song, Over, {\&} Carpenter, 2016). Autistic individuals struggle to reliably differentiate between them (Blampied, Johnston, Miles, {\&} Liberty, 2010; Boraston, Corden, Miles, Skuse, {\&} Blakemore, 2008), which may contribute to their difficulties in understanding others' mental states. An intergroup bias has been found in non-autistic adults in identifying genuine from posed smiles (Young, 2017). This is the first study designed to investigate if autistic individuals would show a different pattern when differentiating smiles for in-groups and out-groups. Fifty-nine autistic adults were compared with forty non-autistic adults, matched on sex, age and nonverbal IQ. Roughly, half of each group were further randomly separated into two groups with a minimal group paradigm (adapted from Howard {\&} Rothbart, 1980). There was no real difference between the groups, participants were primed to believe they were more similar to their in-groups. The ability to distinguish smiles was assessed on a 7-point Likert scale. We found both autism and non-autism groups rated genuine smiles more genuine than posed smiles and in-groups more genuine than out-groups. Even though both groups identified themselves more as in-group than out-group members, autistic individuals were less likely to than non-autistic individuals. However, autistic participants generally rated smiles as less genuine than non-autistic counterparts. These results indicate that autistic adults are capable of identifying genuine smiles from posed smiles, unlike previous findings; but they may be less convinced of the genuineness of others, which may affect their social communication thereafter. Importantly, autistic adults were equally influenced by social intergroup biases which has the potential to be used in interventions to alleviate their social difficulties in daily lives.

null

22,698

inproceedings

trouvain-etal-2022-inhalation

Inhalation Noises as Endings of Laughs in Conversational Speech

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.8/

Trouvain, J{\"urgen and Werner, Raphael and Truong, Khiet

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

28--29

In this study we investigate the role of inhalation noises at the end of laughter events in two conversational corpora that provide relevant annotations. A re-annotation of the categories for laughter, silence and inbreath noises enabled us to see that inhalation noises terminate laughter events in the majority of all inspected laughs with a duration comparable to inbreath noises initiating speech phases. This type of corpus analysis helps to understand the mechanisms of audible respiratory activities in speaking vs. laughing in conversations.

null

22,699

inproceedings

hammoudeh-etal-2022-body

Are There Any Body-movement Differences between Women and Men When They Laugh?

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.9/

Hammoudeh, Ahmad and Maiorca, Antoine and Dupont, St{\'e}phane and Dutoit, Thierry

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

30--31

Smiling differences between men and women have been studied in psychology. Women smile more than men although the expressiveness of women is not universally more across all facial actions. There are also body movement differences between women and men. For example, more open-body postures were reported for men, but are there any body-movement differences between men and women when they laugh? To investigate this question, we study body-movement signals extracted from recorded laughter videos using a deep learning pose estimation model. Initial results showed a higher Fourier Transform amplitude of thorax and shoulder movements for females while males had a higher Fourier transform amplitude of Elbow movement. The differences were not limited to a small frequency range but covered most of the frequency spectrum. However, further investigations are still needed.

null

22,700

inproceedings

rychlowska-etal-2022-laughter

Laughter During Cooperative and Competitive Games

Mazzocconi, Chiara and Haddad, Kevin El and Pelachaud, Catherine and McKeown, Gary

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.smila-1.10/

Rychlowska, Magdalena and McKeown, Gary and Sneddon, Ian and Curran, William

Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

32--34

This exploratory study investigates the extent to which social context influences the frequency of laughter. In a within-subjects design, dyads of strangers played two simple laughter-inducing games in a cooperative and competitive setting, ostensibly to earn money individually and as a team. We examined the frequency of laughs produced in both settings. The analysis revealed that, the effects of cooperative versus competitive framing interacted with the game. Specifically, when playing a general knowledge quiz, participants tended to laugh more in the cooperative than in the competitive setting. However, the opposite was true when participants were asked to find a specific number of poker chips under time pressure. During this task participants laughed more in a competitive than in the cooperative setting. Further analyses revealed that familiarity with the task affected the amount of laughter differently for each of the two tasks. Playing the second round of the poker chips task was associated with a significant decreases in laughter frequency compared to the first round. This effect was less marked for the general knowledge quiz, where increased familiarity with the task in the second round led to more laughs in the cooperative, but not competitive setting. Together, the results highlight the flexibility of laughter as an interaction signal and illustrate the challenges of studying laughter in naturalistic settings.

null

22,701

inproceedings

bigand-etal-2022-synthesis

Synthesis for the Kinematic Control of Identity in Sign Language

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.1/

Bigand, F{\'e}lix and Prigent, Elise and Braffort, Annelies

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

1--6

Sign Language (SL) animations generated from motion capture (mocap) of real signers convey critical information about their identity. It has been suggested that this information is mostly carried by statistics of the movements kinematics. Manipulating these statistics in the generation of SL movements could allow controlling the identity of the signer, notably to preserve anonymity. This paper tests this hypothesis by presenting a novel synthesis algorithm that manipulates the identity-specific statistics of mocap recordings. The algorithm produced convincing new versions of French Sign Language discourses, which accurately modulated the identity prediction of a machine learning model. These results open up promising perspectives toward the automatic control of identity in the motion animation of virtual signers.

null

22,703

inproceedings

choudhury-2022-analysis

Analysis of Torso Movement for Signing Avatar Using Deep Learning

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.2/

Choudhury, Shatabdi

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

7--12

Avatars are virtual or on-screen representations of a human used in various roles for sign language display, including translation and educational tools. Though the ability of avatars to portray acceptable sign language with believable human-like motion has improved in recent years, many still lack the naturalness and supporting motions of human signing. Such details are generally not included in the linguistic annotation. Nevertheless, these motions are highly essential to displaying lifelike and communicative animations. This paper presents a deep learning model for use in a signing avatar. The study focuses on coordinating torso movements and other human body parts. The proposed model will automatically compute the torso rotation based on the avatar`s wrist positions. The resulting motion can improve the user experience and engagement with the avatar.

null

22,704

inproceedings

dafnis-etal-2022-isolated

Isolated Sign Recognition using {ASL} Datasets with Consistent Text-based Gloss Labeling and Curriculum Learning

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.3/

Dafnis, Konstantinos M. and Chroni, Evgenia and Neidle, Carol and Metaxas, Dimitri

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

13--20

We present a new approach for isolated sign recognition, which combines a spatial-temporal Graph Convolution Network (GCN) architecture for modeling human skeleton keypoints with late fusion of both the forward and backward video streams, and we explore the use of curriculum learning. We employ a type of curriculum learning that dynamically estimates, during training, the order of difficulty of each input video for sign recognition; this involves learning a new family of data parameters that are dynamically updated during training. The research makes use of a large combined video dataset for American Sign Language (ASL), including data from both the American Sign Language Lexicon Video Dataset (ASLLVD) and the Word-Level American Sign Language (WLASL) dataset, with modified gloss labeling of the latter{---}to ensure 1-1 correspondence between gloss labels and distinct sign productions, as well as consistency in gloss labeling across the two datasets. This is the first time that these two datasets have been used in combination for isolated sign recognition research. We also compare the sign recognition performance on several different subsets of the combined dataset, varying in, e.g., the minimum number of samples per sign (and therefore also in the total number of sign classes and video examples).

null

22,705

inproceedings

dauriac-etal-2022-example

Example-based Multilinear Sign Language Generation from a Hierarchical Representation

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.4/

Dauriac, Boris and Braffort, Annelies and Bertin-Lem{\'e}e, Elise

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

21--28

This article presents an original method for automatic generation of sign language (SL) content by means of the animation of an avatar, with the aim of creating animations that respect as much as possible linguistic constraints while keeping bio-realistic properties. This method is based on the use of a domain-specific bilingual corpus richly annotated with timed alignments between SL motion capture data, text and hierarchical expressions from the framework called AZee at subsentential level. Animations representing new SL content are built from blocks of animations present in the corpus and adapted to the context if necessary. A smart blending approach has been designed that allows the concatenation, replacement and adaptation of original animation blocks. This approach has been tested on a tailored testset to show as a proof of concept its potential in comprehensibility and fluidity of the animation, as well as its current limits.

null

22,706

inproceedings

deshpande-etal-2022-fine

Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.5/

Deshpande, Neha and Nunnari, Fabrizio and Avramidis, Eleftherios

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

29--38

In this paper, we investigate the capability of convolutional neural networks to recognize in sign language video frames the six basic Ekman facial expressions for {\textquoteleft}fear', {\textquoteleft}disgust', {\textquoteleft}surprise', {\textquoteleft}sadness', {\textquoteleft}happiness', {\textquoteleft}anger' along with the {\textquoteleft}neutral' class. Given the limited amount of annotated facial expression data for the sign language domain, we started from a model pre-trained on general-purpose facial expression datasets and we applied various machine learning techniques such as fine-tuning, data augmentation, class balancing, as well as image preprocessing to reach a better accuracy. The models were evaluated using K-fold cross-validation to get more accurate conclusions. It is experimentally demonstrated that fine-tuning a pre-trained model along with data augmentation by horizontally flipping images and image normalization, helps in providing the best accuracy on the sign language dataset. The best setting achieves satisfactory classification accuracy, comparable to state-of-the-art systems in generic facial expression recognition. Experiments were performed using different combinations of the above-mentioned techniques based on two different architectures, namely MobileNet and EfficientNet, and is deemed that both architectures seem equally suitable for the purpose of fine-tuning, whereas class balancing is discouraged.

null

22,707

inproceedings

dimou-etal-2022-signing

Signing Avatar Performance Evaluation within {EASIER} Project

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.6/

Dimou, Athanasia-Lida and Papavassiliou, Vassilis and McDonald, John and Goulas, Theodore and Vasilaki, Kyriaki and Vacalopoulou, Anna and Fotinea, Stavroula-Evita and Efthimiou, Eleni and Wolfe, Rosalee

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

39--44

The direct involvement of deaf users in the development and evaluation of signing avatars is imperative to achieve legibility and raise trust among synthetic signing technology consumers. A paradigm of constructive cooperation between researchers and the deaf community is the EASIER project , where user driven design and technology development have already started producing results. One major goal of the project is the direct involvement of sign language (SL) users at every stage of development of the project`s signing avatar. As developers wished to consider every parameter of SL articulation including affect and prosody in developing the EASIER SL representation engine, it was necessary to develop a steady communication channel with a wide public of SL users who may act as evaluators and can provide guidance throughout research steps, both during the project`s end-user evaluation cycles and beyond. To this end, we have developed a questionnaire-based methodology, which enables researchers to reach signers of different SL communities on-line and collect their guidance and preferences on all aspects of SL avatar animation that are under study. In this paper, we report on the methodology behind the application of the EASIER evaluation framework for end-user guidance in signing avatar development as it is planned to address signers of four SLs -Greek Sign Language (GSL), French Sign Language (LSF), German Sign Language (DGS) and Swiss German Sign Language (DSGS)- during the first project evaluation cycle. We also briefly report on some interesting findings from the pilot implementation of the questionnaire with content from the Greek Sign Language (GSL).

null

22,708

inproceedings

holmes-etal-2022-improving

Improving Signer Independent Sign Language Recognition for Low Resource Languages

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.7/

Holmes, Ruth and Rushe, Ellen and Fowley, Frank and Ventresque, Anthony

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

45--52

The reliance of deep learning algorithms on large scale datasets represents a significant challenge when learning from low resource sign language datasets. This challenge is compounded when we consider that, for a model to be effective in the real world, it must not only learn the variations of a given sign, but also learn to be invariant to the person signing. In this paper, we first illustrate the performance gap between signer-independent and signer-dependent models on Irish Sign Language manual hand shape data. We then evaluate the effect of transfer learning, with different levels of fine-tuning, on the generalisation of signer independent models, and show the effects of different input representations, namely variations in image data and pose estimation. We go on to investigate the sensitivity of current pose estimation models in order to establish their limitations and areas in need of improvement. The results show that accurate pose estimation outperforms raw RGB image data, even when relying on pre-trained image models. Following on from this, we investigate image texture as a potential contributing factor to the gap in performance between signer-dependent and signer-independent models using counterfactual testing images and discuss potential ramifications for low-resource sign languages. Keywords: Sign language recognition, Transfer learning, Irish Sign Language, Low-resource languages

null

22,709

inproceedings

johnson-2022-improved

Improved Facial Realism through an Enhanced Representation of Anatomical Behavior in Sign Language Avatars

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.8/

Johnson, Ronan

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

53--58

Facial movements and expressions are critical features of signed languages, yet are some of the most challenging to reproduce on signing avatars. Due to the relative lack of research efforts in this area, the facial capabilities of such avatars have yet to receive the approval of those in the Deaf community. This paper revisits the representations of the human face in signed avatars, specifically those based on parameterized muscle simulation such as FACS and the MPEG-4 file definition. An improved framework based on rotational pivots and pre-defined movements is capable of reproducing realistic, natural gestures and mouthings on sign language avatars. The new approach is more harmonious with the underlying construction of signed avatars, generates improved results, and allows for a more intuitive workflow for the artists and animators who interact with the system.

null

22,710

inproceedings

huerta-enochian-etal-2022-kosign

{K}o{S}ign Sign Language Translation Project: Introducing The {NIASL}2021 Dataset

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.9/

Huerta-Enochian, Mathew and Lee, Du Hui and Myung, Hye Jin and Byun, Kang Suk and Lee, Jun Woo

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

59--66

We introduce a new sign language production (SLP) and sign language translation (SLT) dataset, NIASL2021, consisting of 201,026 Korean-KSL data pairs. KSL translations of Korean source texts are represented in three formats: video recordings, keypoint position data, and time-aligned gloss annotations for each hand (using a 7,989 sign vocabulary) and for eight different non-manual signals (NMS). We evaluated our sign language elicitation methodology and found that text-based prompting had a negative effect on translation quality in terms of naturalness and comprehension. We recommend distilling text into a visual medium before translating into sign language or adding a prompt-blind review step to text-based translation methodologies.

null

22,711

inproceedings

mcdonald-etal-2022-novel

A Novel Approach to Managing Lower Face Complexity in Signing Avatars

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.10/

McDonald, John and Johnson, Ronan and Wolfe, Rosalee

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

67--72

An avatar that produces legible, easy-to-understand signing is one of the essential components to an effective automatic signed/spoken translation system. Facial nonmanual signals are essential to natural signing, but unfortunately signing avatars still do not produce acceptable facial expressions, particularly on the lower face. This paper reports on an innovative method to create more realistic lip postures. The approach manages the complexity of creating lip postures, thus making fewer demands on the artists making them. The method will be integral to our efforts to develop libraries containing lip postures to support the generation of facial expressions for several sign languages.

null

22,712

inproceedings

nunnari-2022-software

A Software Toolkit for Pre-processing Sign Language Video Streams

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.11/

Nunnari, Fabrizio

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

73--78

We present the requirements, design guidelines, and the software architecture of an open-source toolkit dedicated to the pre-processing of sign language video material. The toolkit is a collection of functions and command-line tools designed to be integrated with build automation systems. Every pre-processing tool is dedicated to standard pre-processing operations (e.g., trimming, cropping, resizing) or feature extraction (e.g., identification of areas of interest, landmark detection) and can be used also as a standalone Python module. The UML diagrams of its architecture are presented together with a few working examples of its usage. The software is freely available with an open-source license on a public repository.

null

22,713

inproceedings

papadimitriou-etal-2022-greek

{G}reek {S}ign {L}anguage Recognition for the {SL}-{R}e{D}u Learning Platform

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.12/

Papadimitriou, Katerina and Potamianos, Gerasimos and Sapountzaki, Galini and Goulas, Theodore and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Maragos, Petros

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

79--84

There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners' SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign Language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively.

null

22,714

inproceedings

quandt-etal-2022-signing

Signing Avatars in a New Dimension: Challenges and Opportunities in Virtual Reality

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.13/

Quandt, Lorna and Lamberton, Jason and Leannah, Carly and Willis, Athena and Malzkuhn, Melissa

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

85--90

With improved and more easily accessible technology, immersive virtual reality (VR) head-mounted devices have become more ubiquitous. As signing avatar technology improves, virtual reality presents a new and relatively unexplored application for signing avatars. This paper discusses two primary ways that signed language can be represented in immersive virtual spaces: 1) Third-person, in which the VR user sees a character who communicates in signed language; and 2) First-person, in which the VR user produces signed content themselves, tracked by the head-mounted device and visible to the user herself (and/or to other users) in the virtual environment. We will discuss the unique affordances granted by virtual reality and how signing avatars might bring accessibility and new opportunities to virtual spaces. We will then discuss the limitations of signed con-tent in virtual reality concerning virtual signers shown from both third- and first-person perspectives.

null

22,715

inproceedings

saenz-2022-mouthing

Mouthing Recognition with {O}pen{P}ose in Sign Language

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.14/

Saenz, Maria Del Carmen

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

91--94

Many avatars focus on the hands and how they express sign language. However, sign language also uses mouth and face gestures to modify verbs, adjectives, or adverbs; these are known as non-manual components of the sign. To have a translation system that the Deaf community will accept, we need to include these non-manual signs. Just as machine learning is being used on generating hand signs, the work we are focusing on will be doing the same, but with mouthing and mouth gestures. We will be using data from The National Center for Sign Language and Gesture Resources. The data from the center are videos of native signers focusing on different areas of signer movement, gesturing, and mouthing, and are annotated specifically for mouthing studies. With this data, we will run a pre-trained Neural Network application called OpenPose. After running through OpenPose, further analysis of the data is conducted using a Random Forest Classifier. This research looks at how well an algorithm can be trained to spot certain mouthing points and output the mouth annotations with a high degree of accuracy. With this, the appropriate mouthing for animated signs can be easily applied to avatar technologies.

null

22,716

inproceedings

saunders-etal-2022-skeletal

Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.15/

Saunders, Ben and Camg{\"oz, Necati Cihan and Bowden, Richard

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

95--102

Recent approaches to Sign Language Production (SLP) have adopted spoken language Neural Machine Translation (NMT) architectures, applied without sign-specific modifications. In addition, these works represent sign language as a sequence of skeleton pose vectors, projected to an abstract representation with no inherent skeletal structure. In this paper, we represent sign language sequences as a skeletal graph structure, with joints as nodes and both spatial and temporal connections as edges. To operate on this graphical structure, we propose Skeletal Graph Self-Attention (SGSA), a novel graphical attention layer that embeds a skeleton inductive bias into the SLP model. Retaining the skeletal feature representation throughout, we directly apply a spatio-temporal adjacency matrix into the self-attention formulation. This provides structure and context to each skeletal joint that is not possible when using a non-graphical abstract representation, enabling fluid and expressive sign language production. We evaluate our Skeletal Graph Self-Attention architecture on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset, achieving state-of-the-art back translation performance with an 8{\%} and 7{\%} improvement over competing methods for the dev and test sets.

null

22,717

inproceedings

sharma-filhol-2022-multi

Multi-track Bottom-Up Synthesis from Non-Flattened {AZ}ee Scores

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.16/

Sharma, Paritosh and Filhol, Michael

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

103--108

We present an algorithm to improve the pre-existing bottom-up animation system for AZee descriptions to synthesize sign language utterances. Our algorithm allows us to synthesize AZee descriptions by preserving the dynamics of underlying blocks. This bottom-up approach aims to deliver procedurally generated animations capable of generating any sign language utterance if an equivalent AZee description exists. The proposed algorithm is built upon the modules of an open-source animation toolkit and takes advantage of the integrated inverse kinematics solver and a non-linear editor.

null

22,718

inproceedings

van-gemert-etal-2022-first

First Steps Towards a Signing Avatar for Railway Travel Announcements in the {N}etherlands

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.17/

Van Gemert, Britt and Cokart, Richard and Esselink, Lyke and De Meulder, Maartje and Sijm, Nienke and Roelofsen, Floris

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

109--116

This paper presents first steps towards a sign language avatar for communicating railway travel announcements in Dutch Sign Language. Taking an interdisciplinary approach, it demonstrates effective ways to employ co-design and focus group methods in the context of developing sign language technology, and presents several concrete findings and results obtained through co-design and focus group sessions which have not only led to improvements of our own prototype but may also inform the development of signing avatars for other languages and in other application domains.

null

22,719

inproceedings

walsh-etal-2022-changing

Changing the Representation: Examining Language Representation for Neural Sign Language Production

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.18/

Walsh, Harry and Saunders, Ben and Bowden, Richard

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

117--124

Neural Sign Language Production (SLP) aims to automatically translate from spoken language sentences to sign language videos. Historically the SLP task has been broken into two steps; Firstly, translating from a spoken language sentence to a gloss sequence and secondly, producing a sign language video given a sequence of glosses. In this paper we apply Natural Language Processing techniques to the first step of the SLP pipeline. We use language models such as BERT and Word2Vec to create better sentence level embeddings, and apply several tokenization techniques, demonstrating how these improve performance on the low resource translation task of Text to Gloss. We introduce Text to HamNoSys (T2H) translation, and show the advantages of using a phonetic representation for sign language translation rather than a sign level gloss representation. Furthermore, we use HamNoSys to extract the hand shape of a sign and use this as additional supervision during training, further increasing the performance on T2H. Assembling best practise, we achieve a BLEU-4 score of 26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-art baselines.

null

22,720

inproceedings

wolfe-etal-2022-supporting

Supporting Mouthing in Signed Languages: New innovations and a proposal for future corpus building

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sltat-1.19/

Wolfe, Rosalee and McDonald, John and Johnson, Ronan and Sturr, Ben and Klinghoffer, Syd and Bonzani, Anthony and Alexander, Andrew and Barnekow, Nicole

Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

125--130

A recurring concern, oft repeated, regarding the quality of signing avatars is the lack of proper facial movements, particularly in actions that involve mouthing. An analysis uncovered three challenges contributing to the problem. The first is a difficulty in devising an algorithmic strategy for generating mouthing due to the rich variety of mouthings in sign language. For example, part or all of a spoken word may be mouthed depending on the sign language, the syllabic structure of the mouthed word, as well as the register of address and discourse setting. The second challenge was technological. Previous efforts to create avatar mouthing have failed to model the timing present in mouthing or have failed to properly model the mouth`s appearance. The third challenge is one of usability. Previous editing systems, when they existed, were time-consuming to use. This paper describes efforts to improve avatar mouthing by addressing these challenges, resulting in a new approach for mouthing animation. The paper concludes by proposing an experiment in corpus building using the new approach.

null

22,721

inproceedings

roark-gutkin-2022-design

Design principles of an open-source language modeling microservice package for {AAC} text-entry applications

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.1/

Roark, Brian and Gutkin, Alexander

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

1--16

We present MozoLM, an open-source language model microservice package intended for use in AAC text-entry applications, with a particular focus on the design principles of the library. The intent of the library is to allow the ensembling of multiple diverse language models without requiring the clients (user interface designers, system users or speech-language pathologists) to attend to the formats of the models. Issues around privacy, security, dynamic versus static models, and methods of model combination are explored and specific design choices motivated. Some simulation experiments demonstrating the benefits of personalized language model ensembling via the library are presented.

null

10.18653/v1/2022.slpat-1.1

null

22,723

inproceedings

daly-2022-colorcode

{C}olor{C}ode: A {B}ayesian Approach to Augmentative and Alternative Communication with Two Buttons

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.2/

Daly, Matthew

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

17--23

Many people with severely limited muscle control can only communicate through augmentative and alternative communication (AAC) systems with a small number of buttons. In this paper, we present the design for ColorCode, which is an AAC system with two buttons that uses Bayesian inference to determine what the user wishes to communicate. Our information-theoretic analysis of ColorCode simulations shows that it is efficient in extracting information from the user, even in the presence of errors, achieving nearly optimal error correction. ColorCode is provided as open source software.

null

10.18653/v1/2022.slpat-1.2

null

22,724

inproceedings

vaidyanathan-etal-2022-glimpse

A glimpse of assistive technology in daily life

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.3/

Vaidyanathan, Preethi and Wislon, Angela and Sawyer, Doug and Diego, Amy and Webster, Augustine and Fassov, Katerina and Brinton, James and Rubenstein, Jenn

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

24--29

Robitaille (2010) wrote {\textquoteleft}if all technology companies have accessibility in their mind then people with disabilities won`t be left behind.' Current technology has come a long way from where it stood decades ago; however, researchers and manufacturers often do not include people with disabilities in the design process and tend to accommodate them after the fact. In this paper we share feedback from four assistive technology users who rely on one or more assistive technology devices in their everyday lives. We believe end users should be part of the design process and that by bringing together experts and users, we can bridge the research/practice gap.

null

10.18653/v1/2022.slpat-1.3

null

22,725

inproceedings

riad-etal-2022-comparison

A comparison study on patient-psychologist voice diarization

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.4/

Riad, Rachid and Titeux, Hadrien and Lemoine, Laurie and Montillot, Justine and Sliwinski, Agnes and Bagnou, Jennifer and Cao, Xuan and Bachoud-Levi, Anne-Catherine and Dupoux, Emmanuel

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

30--36

Conversations between a clinician and a patient, in natural conditions, are valuable sources of information for medical follow-up. The automatic analysis of these dialogues could help extract new language markers and speed up the clinicians' reports. Yet, it is not clear which model is the most efficient to detect and identify the speaker turns, especially for individuals with speech disorders. Here, we proposed a split of the data that allows conducting a comparative evaluation of different diarization methods. We designed and trained end-to-end neural network architectures to directly tackle this task from the raw signal and evaluate each approach under the same metric. We also studied the effect of fine-tuning models to find the best performance. Experimental results are reported on naturalistic clinical conversations between Psychologists and Interviewees, at different stages of Huntington`s disease, displaying a large panel of speech disorders. We found out that our best end-to-end model achieved 19.5 {\%} IER on the test set, compared to 23.6{\%} achieved by the finetuning of the X-vector architecture. Finally, we observed that we could extract clinical markers directly from the automatic systems, highlighting the clinical relevance of our methods.

null

10.18653/v1/2022.slpat-1.4

null

22,726

inproceedings

gerlach-etal-2022-producing

Producing {S}tandard {G}erman Subtitles for {S}wiss {G}erman {TV} Content

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.5/

Gerlach, Johanna and Mutal, Jonathan and Bouillon, Pierrette

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

37--43

In this study we compare two approaches (neural machine translation and edit-based) and the use of synthetic data for the task of translating normalised Swiss German ASR output into correct written Standard German for subtitles, with a special focus on syntactic differences. Results suggest that NMT is better suited to this task and that relatively simple rule-based generation of training data could be a valuable approach for cases where little training data is available and transformations are simple.

null

10.18653/v1/2022.slpat-1.5

null

22,727

inproceedings

norre-etal-2022-investigating

Investigating the Medical Coverage of a Translation System into Pictographs for Patients with an Intellectual Disability

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.6/

Norr{\'e}, Magali and Vandeghinste, Vincent and Fran{\c{c}}ois, Thomas and Bouillon, Pierrette

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

44--49

Communication between physician and patients can lead to misunderstandings, especially for disabled people. An automatic system that translates natural language into a pictographic language is one of the solutions that could help to overcome this issue. In this preliminary study, we present the French version of a translation system using the Arasaac pictographs and we investigate the strategies used by speech therapists to translate into pictographs. We also evaluate the medical coverage of this tool for translating physician questions and patient instructions.

null

10.18653/v1/2022.slpat-1.6

null

22,728

inproceedings

gooding-2022-ethical

On the Ethical Considerations of Text Simplification

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.7/

Gooding, Sian

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

50--57

This paper outlines the ethical implications of text simplification within the framework of assistive systems. We argue that a distinction should be made between the technologies that perform text simplification and the realisation of these in assistive technologies. When using the latter as a motivation for research, it is important that the subsequent ethical implications be carefully considered. We provide guidelines for the framing of text simplification independently of assistive systems, as well as suggesting directions for future research and discussion based on the concerns raised.

null

10.18653/v1/2022.slpat-1.7

null

22,729

inproceedings

herold-etal-2022-applying

Applying the Stereotype Content Model to assess disability bias in popular pre-trained {NLP} models underlying {AI}-based assistive technologies

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.8/

Herold, Brienna and Waller, James and Kushalnagar, Raja

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

58--65

Stereotypes are a positive or negative, generalized, and often widely shared belief about the attributes of certain groups of people, such as people with sensory disabilities. If stereotypes manifest in assistive technologies used by deaf or blind people, they can harm the user in a number of ways, especially considering the vulnerable nature of the target population. AI models underlying assistive technologies have been shown to contain biased stereotypes, including racial, gender, and disability biases. We build on this work to present a psychology-based stereotype assessment of the representation of disability, deafness, and blindness in BERT using the Stereotype Content Model. We show that BERT contains disability bias, and that this bias differs along established stereotype dimensions.

null

10.18653/v1/2022.slpat-1.8

null

22,730

inproceedings

h-kumar-etal-2022-cuebot

{C}ue{B}ot: Cue-Controlled Response Generation for Assistive Interaction Usages

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.9/

H. Kumar, Shachi and Su, Hsuan and Manuvinakurike, Ramesh and Pinaroc, Max and Prasad, Sai and Sahay, Saurav and Nachman, Lama

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

66--79

Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle. Language model technology can play a huge role in empowering these users and help them interact with others with less effort via interaction support. To enable this population, we build a system that can represent them in a social conversation and generate responses that can be controlled by the users using cues/keywords. We build models that can speed up this communication by suggesting relevant cues in the dialog response context. We also introduce a keyword-loss to lexically constrain the model response output. We present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system to show that our models perform significantly better than models without control. Our evaluation and user study shows that keyword-control on end-to-end response generation models is powerful and can enable and empower users with degenerative disorders to carry out their day-to-day communication.

null

10.18653/v1/2022.slpat-1.9

null

22,731

inproceedings

ni-chasaide-etal-2022-challenges

Challenges in assistive technology development for an endangered language: an {I}rish ({G}aelic) perspective

Ebling, Sarah and Prud{'}hommeaux, Emily and Vaidyanathan, Preethi

may

2022

Dublin, Ireland

Association for Computational Linguistics

https://aclanthology.org/2022.slpat-1.10/

Ni Chasaide, Ailbhe and Barnes, Emily and N{\'i} Chiar{\'a}in, Neasa and McGuirk, Ronan and Morrin, Ois{\'i}n and Nic Corcr{\'a}in, Muireann and Cummins, Julia

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

80--87

This paper describes three areas of assistive technology development which deploy the resources and speech technology for Irish (Gaelic), newly emerging from the ABAIR initiative. These include (i) a screenreading facility for visually impaired people, (ii) an application to help develop phonological awareness and early literacy for dyslexic people (iii) a speech-enabled AAC system for non-speaking people. Each of these is at a different stage of development and poses unique challenges: these are dis-cussed along with the approaches adopted to address them. Three guiding principles underlie development. Firstly, the sociolinguistic context and the needs of the community are essential considerations in setting priorities. Secondly, development needs to be language sensitive. The need for skilled researchers with a deep knowledge of Irish structure is illustrated in the case of (ii) and (iii), where aspects of Irish linguistic structure (phonological, morphological and grammatical) and the striking differences from English pose challenges for systems aimed at bilingual Irish-English users. Thirdly, and most importantly, the users and their support networks are central {--} not as passive recipients of ready-made technologies, but as active partners at every stage of development, from design to implementation, evaluation and dissemination.

null

10.18653/v1/2022.slpat-1.10

null

22,732

inproceedings

zanon-boito-etal-2022-unsupervised

Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.1/

Zanon Boito, Marcely and Yusuf, Bolaji and Ondel, Lucas and Villavicencio, Aline and Besacier, Laurent

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

1--9

Documenting languages helps to prevent the extinction of endangered dialects - many of which are otherwise expected to disappear by the end of the century. When documenting oral languages, unsupervised word segmentation (UWS) from speech is a useful, yet challenging, task. It consists in producing time-stamps for slicing utterances into smaller segments corresponding to words, being performed from phonetic transcriptions, or in the absence of these, from the output of unsupervised speech discretization models. These discretization models are trained using raw speech only, producing discrete speech units that can be applied for downstream (text-based) tasks. In this paper we compare five of these models: three Bayesian and two neural approaches, with regards to the exploitability of the produced units for UWS. For the UWS task, we experiment with two models, using as our target language the Mboshi (Bantu C25), an unwritten language from Congo-Brazzaville. Additionally, we report results for Finnish, Hungarian, Romanian and Russian in equally low-resource settings, using only 4 hours of speech. Our results suggest that neural models for speech discretization are difficult to exploit in our setting, and that it might be necessary to adapt them to limit sequence length. We obtain our best UWS results by using Bayesian models that produce high quality, yet compressed, discrete representations of the input speech signal.

null

22,734

inproceedings

fong-etal-2022-open

An Open Source Web Reader for Under-Resourced Languages

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.2/

Fong, Judy and Gunnarsson, {\THorsteinn Da{\dhi and {\THorsteinsd{\'ottir, Sunneva and {\"Orn{\'olfsson, Gunnar Thor and Gudnason, Jon

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

10--15

We have developed an open source web reader in Iceland for under-resourced languages. The web reader was developed due to the need for a free and good quality web reader for languages which fall outside the scope of commercially available web readers. It relies on a text-to-speech (TTS) pipeline accessed via a cloud service. The web reader was developed using the Icelandic TTS voices Alfur and Dilja, but could be connected to any language which has a TTS pipeline. The design of our web reader focuses on functionality, adaptability and user friendliness. Therefore, the web reader`s feature set heavily overlaps with the minimal features necessary to provide a good web reading experience while still being extensible enough to be adapted to work for other languages, high-resourced and under-resourced. The web reader works well on all the major web browsers and has a Web Content Accessibility Guidelines 2.0 Level AA: Acceptable compliance, meaning that it works well for the largest user groups, people in under-resourced languages with visual impairments and difficulty reading. The code for our web reader is available and published with an Apache 2.0 license at \url{https://github.com/cadia-lvl/WebRICE}, which includes a simple demo of the project.

null

22,735

inproceedings

do-etal-2022-text

Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.3/

Do, Phat and Coler, Matt and Dijkstra, Jelske and Klabbers, Esther

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

16--22

We propose a new approach for phoneme mapping in cross-lingual transfer learning for text-to-speech (TTS) in under-resourced languages (URLs), using phonological features from the PHOIBLE database and a language-independent mapping rule. This approach was validated through our experiment, in which we pre-trained acoustic models in Dutch, Finnish, French, Japanese, and Spanish, and fine-tuned them with 30 minutes of Frisian training data. The experiment showed an improvement in both naturalness and pronunciation accuracy in the synthesized Frisian speech when our mapping approach was used. Since this improvement also depended on the source language, we then experimented on finding a good criterion for selecting source languages. As an alternative to the traditionally used language family criterion, we tested a novel idea of using Angular Similarity of Phoneme Frequencies (ASPF), which measures the similarity between the phoneme systems of two languages. ASPF was empirically confirmed to be more effective than language family as a criterion for source language selection, and also to affect the phoneme mapping`s effectiveness. Thus, a combination of our phoneme mapping approach and the ASPF measure can be beneficially adopted by other studies involving multilingual or cross-lingual TTS for URLs.

null

22,736

inproceedings

littell-etal-2022-readalong

{R}ead{A}long Studio: Practical Zero-Shot Text-Speech Alignment for Indigenous Language Audiobooks

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.4/

Littell, Patrick and Joanis, Eric and Pine, Aidan and Tessier, Marc and Huggins Daines, David and Torkornoo, Delasie

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

23--32

While the alignment of audio recordings and text (often termed {\textquotedblleft}forced alignment{\textquotedblright}) is often treated as a solved problem, in practice the process of adapting an alignment system to a new, under-resourced language comes with significant challenges, requiring experience and expertise that many outside of the speech community lack. This puts otherwise {\textquotedblleft}solvable{\textquotedblright} problems, like the alignment of Indigenous language audiobooks, out of reach for many real-world Indigenous language organizations. In this paper, we detail ReadAlong Studio, a suite of tools for creating and visualizing aligned audiobooks, including educational features like time-aligned highlighting, playing single words in isolation, and variable-speed playback. It is intended to be accessible to creators without an extensive background in speech or NLP, by automating or making optional many of the specialist steps in an alignment pipeline. It is well documented at a beginner-technologist level, has already been adapted to 30 languages, and can work out-of-the-box on many more languages without adaptation.

null

22,737

inproceedings

hegde-etal-2022-corpus

Corpus Creation for Sentiment Analysis in Code-Mixed {T}ulu Text

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.5/

Hegde, Asha and Anusha, Mudoor Devadas and Coelho, Sharal and Shashirekha, Hosahalli Lakshmaiah and Chakravarthi, Bharathi Raja

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

33--40

Sentiment Analysis (SA) employing code-mixed data from social media helps in getting insights to the data and decision making for various applications. One such application is to analyze users' emotions from comments of videos on YouTube. Social media comments do not adhere to the grammatical norms of any language and they often comprise a mix of languages and scripts. The lack of annotated code-mixed data for SA in a low-resource language like Tulu makes the SA a challenging task. To address the lack of annotated code-mixed Tulu data for SA, a gold standard trlingual code-mixed Tulu annotated corpus of 7,171 YouTube comments is created. Further, Machine Learning (ML) algorithms are employed as baseline models to evaluate the developed dataset and the performance of the ML algorithms are found to be encouraging.

null

22,738

inproceedings

hutin-allassonniere-tang-2022-crowd

Crowd-sourcing for Less-resourced Languages: Lingua Libre for {P}olish

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.6/

Hutin, Mathilde and Allassonni{\`e}re-Tang, Marc

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

41--47

Oral corpora for linguistic inquiry are frequently built based on the content of news, radio, and/or TV shows, sometimes also of laboratory recordings. Most of these existing corpora are restricted to languages with a large amount of data available. Furthermore, such corpora are not always accessible under a free open-access license. We propose a crowd-sourced alternative to this gap. Lingua Libre is the participatory linguistic media library hosted by Wikimedia France. It includes recordings from more than 140 languages. These recordings have been provided by more than 750 speakers worldwide, who voluntarily recorded word entries of their native language and made them available under a Creative Commons license. In the present study, we take Polish, a less-resourced language in terms of phonetic data, as an example, and compare our phonetic observations built on the data from Lingua Libre with the phonetic observations found by previous linguistic studies. We observe that the data from Lingua Libre partially matches the phonetic inventory of Polish as described in previous studies, but that the acoustic values are less precise, thus showing both the potential and the limitations of Lingua Libre to be used for phonetic research.

null

22,739

inproceedings

martin-rodriguez-etal-2022-tupian

Tup{\'i}an Language Ressources: Data, Tools, Analyses

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.7/

Mart{\'i}n Rodr{\'i}guez, Lorena and Merzhevich, Tatiana and Silva, Wellington and Tresoldi, Tiago and Aragon, Carolina and Gerardi, Fabr{\'i}cio F.

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

48--58

TuLaR (Tupian Language Resources) is a project for collecting, documenting, analyzing, and developing computational and pedagogical material for low-resource Brazilian indigenous languages. It provides valuable data for language research regarding typological, syntactic, morphological, and phonological aspects. Here we present TuLaR`s databases, with special consideration to TuDeT (Tupian Dependency Treebanks), an annotated corpus under development for nine languages of the Tupian family, built upon the Universal Dependencies framework. The annotation within such a framework serves a twofold goal: enriching the linguistic documentation of the Tupian languages due to the rapid and consistent annotation, and providing computational resources for those languages, thanks to the suitability of our framework for developing NLP tools. We likewise present a related lexical database, some tools developed by the project, and examine future goals for our initiative.

null

22,740

inproceedings

de-gibert-bonet-etal-2022-quality

Quality versus Quantity: Building {C}atalan-{E}nglish {MT} Resources

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.8/

de Gibert Bonet, Ona and Kharitonova, Ksenia and Calvo Figueras, Blanca and Armengol-Estap{\'e}, Jordi and Melero, Maite

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

59--69

In this work, we make the case of quality over quantity when training a MT system for a medium-to-low-resource language pair, namely Catalan-English. We compile our training corpus out of existing resources of varying quality and a new high-quality corpus. We also provide new evaluation translation datasets in three different domains. In the process of building Catalan-English parallel resources, we evaluate the impact of drastically filtering alignments in the resulting MT engines. Our results show that even when resources are limited, as in this case, it is worth filtering for quality. We further explore the cross-lingual transfer learning capabilities of the proposed model for parallel corpus filtering by applying it to other languages. All resources generated in this work are released under open license to encourage the development of language technology in Catalan.

null

22,741

inproceedings

mabokela-schlippe-2022-sentiment

A Sentiment Corpus for {S}outh {A}frican Under-Resourced Languages in a Multilingual Context

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.9/

Mabokela, Ronny and Schlippe, Tim

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

70--77

Multilingual sentiment analysis is a process of detecting and classifying sentiment based on textual information written in multiple languages. There has been tremendous research advancement on high-resourced languages such as English. However, progress on under-resourced languages remains underrepresented with limited opportunities for further development of natural language processing (NLP) technologies. Sentiment analysis (SA) for under-resourced language still is a skewed research area. Although, there are some considerable efforts in emerging African countries to develop such resources for under-resourced languages, languages such as indigenous South African languages still suffer from a lack of datasets. To the best of our knowledge, there is currently no dataset dedicated to SA research for South African languages in a multilingual context, i.e. comments are in different languages and may contain code-switching. In this paper, we present the first subset of the multilingual sentiment corpus SAfriSenti for the three most widely spoken languages in South Africa{---}English, Sepedi (i.e. Northern Sotho), and Setswana. This subset consists of over 40,000 annotated tweets in all the three languages including even 36.6{\%} of code-switched texts. We present data collection, cleaning and annotation strategies that were followed to curate the dataset for these languages. Furthermore, we describe how we developed language-specific sentiment lexicons, morpheme-based sentiment taggers, conduct linguistic analyses and present possible solutions for the challenges of this sentiment dataset. We will release the dataset and sentiment lexicons to the research communities to advance the NLP research of under-resourced languages.

null

22,742

inproceedings

kvapilikova-bojar-2022-cuni

{CUNI} Submission to {MT}4{A}ll Shared Task

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.10/

Kvapil{\'i}kov{\'a}, Ivana and Bojar, Ondrej

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

78--82

This paper describes our submission to the MT4All Shared Task in unsupervised machine translation from English to Ukrainian, Kazakh and Georgian in the legal domain. In addition to the standard pipeline for unsupervised training (pretraining followed by denoising and back-translation), we used supervised training on a pseudo-parallel corpus retrieved from the provided mono-lingual corpora. Our system scored significantly higher than the baseline hybrid unsupervised MT system.

null

22,743

inproceedings

pimienta-2022-resource

Resource: Indicators on the Presence of Languages in {I}nternet

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.11/

Pimienta, Daniel

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

83--91

Reliable and maintained indicators of the space of languages on the Internet are required to support appropriate public policies and well-informed linguistic studies. Current sources are scarce and often strongly biased. The model to produce indicators on the presence of languages in the Internet, launched by the Observatory in 2017, has reached a sensible level of maturity and its data products are shared in CC-BY-SA 4.0 license. It reaches now 329 languages (L1 speakers {\ensuremath{>}} one million) and all the biases associated with the model have been controlled to an acceptable threshold, giving trust to the data, within an estimated confidence interval of +-20{\%}. Some of the indicators (mainly the percentage of L1+L2 speakers connected to the Internet per language and derivates) rely on Ethnologue Global Dataset {\#}24 for demo-linguistic data and ITU, completed by World Bank, for the percentage of persons connected to the Internet by country. The rest of indicators relies on the previous sources plus a large combination of hundreds of different sources for data related to Web contents per language. This research poster focuses the description of the new linguistic resources created. Methodological considerations are only exposed briefly and will be developed in another paper.

null

22,744

inproceedings

dogruoz-sitaram-2022-language

Language Technologies for Low Resource Languages: Sociolinguistic and Multilingual Insights

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.12/

Do{\u{gru{\"oz, A. Seza and Sitaram, Sunayana

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

92--97

There is a growing interest in building language technologies (LTs) for low resource languages (LRLs). However, there are flaws in the planning, data collection and development phases mostly due to the assumption that LRLs are similar to High Resource Languages (HRLs) but only smaller in size. In our paper, we first provide examples of failed LTs for LRLs and provide the reasons for these failures. Second, we discuss the problematic issues with the data for LRLs. Finally, we provide recommendations for building better LTs for LRLs through insights from sociolinguistics and multilingualism. Our goal is not to solve all problems around LTs for LRLs but to raise awareness about the existing issues, provide recommendations toward possible solutions and encourage collaboration across academic disciplines for developing LTs that actually serve the needs and preferences of the LRL communities.

null

22,745

inproceedings

rakhmanov-schlippe-2022-sentiment

Sentiment Analysis for {H}ausa: Classifying Students' Comments

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.13/

Rakhmanov, Ochilbek and Schlippe, Tim

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

98--105

We describe our work on sentiment analysis for Hausa, where we investigated monolingual and cross-lingual approaches to classify student comments in course evaluations. Furthermore, we propose a novel stemming algorithm to improve accuracy. For studies in this area, we collected a corpus of more than 40,000 comments{---}the Hausa-English Sentiment Analysis Corpus For Educational Environments (HESAC). Our results demonstrate that the monolingual approaches for Hausa sentiment analysis slightly outperform the cross-lingual systems. Using our stemming algorithm in the pre-processing even improved the best model resulting in 97.4{\%} accuracy on HESAC.

null

22,746

inproceedings

maskey-etal-2022-nepali

{N}epali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for {N}epali Text Classification

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.14/

Maskey, Utsav and Bhatta, Manish and Bhatt, Shiva and Dhungel, Sanket and Bal, Bal Krishna

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

106--111

Language model pre-training has significantly impacted NLP and resulted in performance gains on many NLP-related tasks, but comparative study of different approaches on many low-resource languages seems to be missing. This paper attempts to investigate appropriate methods for pretraining a Transformer-based model for the Nepali language. We focus on the language-specific aspects that need to be considered for modeling. Although some language models have been trained for Nepali, the study is far from sufficient. We train three distinct Transformer-based masked language models for Nepali text sequences: distilbert-base (Sanh et al., 2019) for its efficiency and minuteness, deberta-base (P. He et al., 2020) for its capability of modeling the dependency of nearby token pairs and XLM-ROBERTa (Conneau et al., 2020) for its capabilities to handle multilingual downstream tasks. We evaluate and compare these models with other Transformer-based models on a downstream classification task with an aim to suggest an effective strategy for training low-resource language models and their fine-tuning.

null

22,747