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Towards a Decomposable Metric for Explainable Evaluation of Text
Generation from AMR | Systems that generate natural language text from abstract meaning
representations such as AMR are typically evaluated using automatic surface
matching metrics that compare the generated texts to reference texts from which
the input meaning representations were constructed. We show that besides
well-known issues from which such metrics suffer, an additional problem arises
when applying these metrics for AMR-to-text evaluation, since an abstract
meaning representation allows for numerous surface realizations. In this work
we aim to alleviate these issues by proposing $\mathcal{M}\mathcal{F}_\beta$, a
decomposable metric that builds on two pillars. The first is the principle of
meaning preservation $\mathcal{M}$: it measures to what extent a given AMR can
be reconstructed from the generated sentence using SOTA AMR parsers and
applying (fine-grained) AMR evaluation metrics to measure the distance between
the original and the reconstructed AMR. The second pillar builds on a principle
of (grammatical) form $\mathcal{F}$ that measures the linguistic quality of the
generated text, which we implement using SOTA language models. In two extensive
pilot studies we show that fulfillment of both principles offers benefits for
AMR-to-text evaluation, including explainability of scores. Since
$\mathcal{M}\mathcal{F}_\beta$ does not necessarily rely on gold AMRs, it may
extend to other text generation tasks.
| 2,021 | Computation and Language |
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured
Documents for Question Answering | Knowledge graphs (KGs) have the advantage of providing fine-grained detail
for question-answering systems. Unfortunately, building a reliable KG is
time-consuming and expensive as it requires human intervention. To overcome
this issue, we propose a novel framework to automatically construct a KG from
unstructured documents that does not require external alignment. We first
extract surface-form knowledge tuples from unstructured documents and encode
them with contextual information. Entities with similar context semantics are
then linked through internal alignment to form a graph structure. This allows
us to extract the desired information from multiple documents by traversing the
generated KG without a manual process. We examine its performance in retrieval
based QA systems by reformulating the WikiMovies and MetaQA datasets into a
tuple-level retrieval task. The experimental results show that our method
outperforms traditional retrieval methods by a large margin.
| 2,021 | Computation and Language |
Language Models as Knowledge Bases: On Entity Representations, Storage
Capacity, and Paraphrased Queries | Pretrained language models have been suggested as a possible alternative or
complement to structured knowledge bases. However, this emerging LM-as-KB
paradigm has so far only been considered in a very limited setting, which only
allows handling 21k entities whose single-token name is found in common LM
vocabularies. Furthermore, the main benefit of this paradigm, namely querying
the KB using a variety of natural language paraphrases, is underexplored so
far. Here, we formulate two basic requirements for treating LMs as KBs: (i) the
ability to store a large number facts involving a large number of entities and
(ii) the ability to query stored facts. We explore three entity representations
that allow LMs to represent millions of entities and present a detailed case
study on paraphrased querying of world knowledge in LMs, thereby providing a
proof-of-concept that language models can indeed serve as knowledge bases.
| 2,021 | Computation and Language |
Discovering Useful Sentence Representations from Large Pretrained
Language Models | Despite the extensive success of pretrained language models as encoders for
building NLP systems, they haven't seen prominence as decoders for sequence
generation tasks. We explore the question of whether these models can be
adapted to be used as universal decoders. To be considered "universal," a
decoder must have an implicit representation for any target sentence $s$, such
that it can recover that sentence exactly when conditioned on its
representation. For large transformer-based language models trained on vast
amounts of English text, we investigate whether such representations can be
easily discovered using standard optimization methods. We present and compare
three representation injection techniques for transformer-based models and
three accompanying methods which map sentences to and from this representation
space. Experiments show that not only do representations exist for sentences
from a variety of genres. More importantly, without needing complex
optimization algorithms, our methods recover these sentences almost perfectly
without fine-tuning the underlying language model at all.
| 2,020 | Computation and Language |
Controlling Dialogue Generation with Semantic Exemplars | Dialogue systems pretrained with large language models generate locally
coherent responses, but lack the fine-grained control over responses necessary
to achieve specific goals. A promising method to control response generation is
exemplar-based generation, in which models edit exemplar responses that are
retrieved from training data, or hand-written to strategically address
discourse-level goals, to fit new dialogue contexts. But, current
exemplar-based approaches often excessively copy words from the exemplar
responses, leading to incoherent replies. We present an Exemplar-based Dialogue
Generation model, EDGE, that uses the semantic frames present in exemplar
responses to guide generation. We show that controlling dialogue generation
based on the semantic frames of exemplars, rather than words in the exemplar
itself, improves the coherence of generated responses, while preserving
semantic meaning and conversation goals present in exemplar responses.
| 2,021 | Computation and Language |
Do Syntax Trees Help Pre-trained Transformers Extract Information? | Much recent work suggests that incorporating syntax information from
dependency trees can improve task-specific transformer models. However, the
effect of incorporating dependency tree information into pre-trained
transformer models (e.g., BERT) remains unclear, especially given recent
studies highlighting how these models implicitly encode syntax. In this work,
we systematically study the utility of incorporating dependency trees into
pre-trained transformers on three representative information extraction tasks:
semantic role labeling (SRL), named entity recognition, and relation
extraction.
We propose and investigate two distinct strategies for incorporating
dependency structure: a late fusion approach, which applies a graph neural
network on the output of a transformer, and a joint fusion approach, which
infuses syntax structure into the transformer attention layers. These
strategies are representative of prior work, but we introduce additional model
design elements that are necessary for obtaining improved performance. Our
empirical analysis demonstrates that these syntax-infused transformers obtain
state-of-the-art results on SRL and relation extraction tasks. However, our
analysis also reveals a critical shortcoming of these models: we find that
their performance gains are highly contingent on the availability of
human-annotated dependency parses, which raises important questions regarding
the viability of syntax-augmented transformers in real-world applications.
| 2,021 | Computation and Language |
Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life
Anecdotes | As AI systems become an increasing part of people's everyday lives, it
becomes ever more important that they understand people's ethical norms.
Motivated by descriptive ethics, a field of study that focuses on people's
descriptive judgments rather than theoretical prescriptions on morality, we
investigate a novel, data-driven approach to machine ethics.
We introduce Scruples, the first large-scale dataset with 625,000 ethical
judgments over 32,000 real-life anecdotes. Each anecdote recounts a complex
ethical situation, often posing moral dilemmas, paired with a distribution of
judgments contributed by the community members. Our dataset presents a major
challenge to state-of-the-art neural language models, leaving significant room
for improvement. However, when presented with simplified moral situations, the
results are considerably more promising, suggesting that neural models can
effectively learn simpler ethical building blocks.
A key take-away of our empirical analysis is that norms are not always
clean-cut; many situations are naturally divisive. We present a new method to
estimate the best possible performance on such tasks with inherently diverse
label distributions, and explore likelihood functions that separate intrinsic
from model uncertainty.
| 2,021 | Computation and Language |
Inducing Language-Agnostic Multilingual Representations | Cross-lingual representations have the potential to make NLP techniques
available to the vast majority of languages in the world. However, they
currently require large pretraining corpora or access to typologically similar
languages. In this work, we address these obstacles by removing language
identity signals from multilingual embeddings. We examine three approaches for
this: (i) re-aligning the vector spaces of target languages (all together) to a
pivot source language; (ii) removing language-specific means and variances,
which yields better discriminativeness of embeddings as a by-product; and (iii)
increasing input similarity across languages by removing morphological
contractions and sentence reordering. We evaluate on XNLI and reference-free MT
across 19 typologically diverse languages. Our findings expose the limitations
of these approaches -- unlike vector normalization, vector space re-alignment
and text normalization do not achieve consistent gains across encoders and
languages. Due to the approaches' additive effects, their combination decreases
the cross-lingual transfer gap by 8.9 points (m-BERT) and 18.2 points (XLM-R)
on average across all tasks and languages, however. Our code and models are
publicly available.
| 2,021 | Computation and Language |
PTT5: Pretraining and validating the T5 model on Brazilian Portuguese
data | In natural language processing (NLP), there is a need for more resources in
Portuguese, since much of the data used in the state-of-the-art research is in
other languages. In this paper, we pretrain a T5 model on the BrWac corpus, an
extensive collection of web pages in Portuguese, and evaluate its performance
against other Portuguese pretrained models and multilingual models on three
different tasks. We show that our Portuguese pretrained models have
significantly better performance over the original T5 models. Moreover, we
demonstrate the positive impact of using a Portuguese vocabulary. Our code and
models are available at https://github.com/unicamp-dl/PTT5.
| 2,020 | Computation and Language |
VisualSem: A High-quality Knowledge Graph for Vision and Language | An exciting frontier in natural language understanding (NLU) and generation
(NLG) calls for (vision-and-) language models that can efficiently access
external structured knowledge repositories. However, many existing knowledge
bases only cover limited domains, or suffer from noisy data, and most of all
are typically hard to integrate into neural language pipelines. To fill this
gap, we release VisualSem: a high-quality knowledge graph (KG) which includes
nodes with multilingual glosses, multiple illustrative images, and visually
relevant relations. We also release a neural multi-modal retrieval model that
can use images or sentences as inputs and retrieves entities in the KG. This
multi-modal retrieval model can be integrated into any (neural network) model
pipeline. We encourage the research community to use VisualSem for data
augmentation and/or as a source of grounding, among other possible uses.
VisualSem as well as the multi-modal retrieval models are publicly available
and can be downloaded in this URL: https://github.com/iacercalixto/visualsem
| 2,021 | Computation and Language |
Multi-modal Cooking Workflow Construction for Food Recipes | Understanding food recipe requires anticipating the implicit causal effects
of cooking actions, such that the recipe can be converted into a graph
describing the temporal workflow of the recipe. This is a non-trivial task that
involves common-sense reasoning. However, existing efforts rely on hand-crafted
features to extract the workflow graph from recipes due to the lack of
large-scale labeled datasets. Moreover, they fail to utilize the cooking
images, which constitute an important part of food recipes. In this paper, we
build MM-ReS, the first large-scale dataset for cooking workflow construction,
consisting of 9,850 recipes with human-labeled workflow graphs. Cooking steps
are multi-modal, featuring both text instructions and cooking images. We then
propose a neural encoder-decoder model that utilizes both visual and textual
information to construct the cooking workflow, which achieved over 20%
performance gain over existing hand-crafted baselines.
| 2,020 | Computation and Language |
Spatial Language Representation with Multi-Level Geocoding | We present a multi-level geocoding model (MLG) that learns to associate texts
to geographic locations. The Earth's surface is represented using space-filling
curves that decompose the sphere into a hierarchy of similarly sized,
non-overlapping cells. MLG balances generalization and accuracy by combining
losses across multiple levels and predicting cells at each level
simultaneously. Without using any dataset-specific tuning, we show that MLG
obtains state-of-the-art results for toponym resolution on three English
datasets. Furthermore, it obtains large gains without any knowledge base
metadata, demonstrating that it can effectively learn the connection between
text spans and coordinates - and thus can be extended to toponymns not present
in knowledge bases.
| 2,020 | Computation and Language |
GRIT: Generative Role-filler Transformers for Document-level Event
Entity Extraction | We revisit the classic problem of document-level role-filler entity
extraction (REE) for template filling. We argue that sentence-level approaches
are ill-suited to the task and introduce a generative transformer-based
encoder-decoder framework (GRIT) that is designed to model context at the
document level: it can make extraction decisions across sentence boundaries; is
implicitly aware of noun phrase coreference structure, and has the capacity to
respect cross-role dependencies in the template structure. We evaluate our
approach on the MUC-4 dataset, and show that our model performs substantially
better than prior work. We also show that our modeling choices contribute to
model performance, e.g., by implicitly capturing linguistic knowledge such as
recognizing coreferent entity mentions.
| 2,021 | Computation and Language |
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift | We tackle the task of adapting event extractors to new domains without
labeled data, by aligning the marginal distributions of source and target
domains. As a testbed, we create two new event extraction datasets using
English texts from two medical domains: (i) clinical notes, and (ii)
doctor-patient conversations. We test the efficacy of three marginal alignment
techniques: (i) adversarial domain adaptation (ADA), (ii) domain adaptive
fine-tuning (DAFT), and (iii) a novel instance weighting technique based on
language model likelihood scores (LIW). LIW and DAFT improve over a no-transfer
BERT baseline on both domains, but ADA only improves on clinical notes. Deeper
analysis of performance under different types of shifts (e.g., lexical shift,
semantic shift) reveals interesting variations among models. Our
best-performing models reach F1 scores of 70.0 and 72.9 on notes and
conversations respectively, using no labeled data from target domains.
| 2,020 | Computation and Language |
Don't Change Me! User-Controllable Selective Paraphrase Generation | In the paraphrase generation task, source sentences often contain phrases
that should not be altered. Which phrases, however, can be context dependent
and can vary by application. Our solution to this challenge is to provide the
user with explicit tags that can be placed around any arbitrary segment of text
to mean "don't change me!" when generating a paraphrase; the model learns to
explicitly copy these phrases to the output. The contribution of this work is a
novel data generation technique using distant supervision that allows us to
start with a pretrained sequence-to-sequence model and fine-tune a paraphrase
generator that exhibits this behavior, allowing user-controllable paraphrase
generation. Additionally, we modify the loss during fine-tuning to explicitly
encourage diversity in model output. Our technique is language agnostic, and we
report experiments in English and Chinese.
| 2,021 | Computation and Language |
Tweet to News Conversion: An Investigation into Unsupervised
Controllable Text Generation | Text generator systems have become extremely popular with the advent of
recent deep learning models such as encoder-decoder. Controlling the
information and style of the generated output without supervision is an
important and challenging Natural Language Processing (NLP) task. In this
paper, we define the task of constructing a coherent paragraph from a set of
disaster domain tweets, without any parallel data. We tackle the problem by
building two systems in pipeline. The first system focuses on unsupervised
style transfer and converts the individual tweets into news sentences. The
second system stitches together the outputs from the first system to form a
coherent news paragraph. We also propose a novel training mechanism, by
splitting the sentences into propositions and training the second system to
merge the sentences. We create a validation and test set consisting of
tweet-sets and their equivalent news paragraphs to perform empirical
evaluation. In a completely unsupervised setting, our model was able to achieve
a BLEU score of 19.32, while successfully transferring styles and joining
tweets to form a meaningful news paragraph.
| 2,020 | Computation and Language |
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing
Benchmark | Scaling semantic parsing models for task-oriented dialog systems to new
languages is often expensive and time-consuming due to the lack of available
datasets. Available datasets suffer from several shortcomings: a) they contain
few languages b) they contain small amounts of labeled examples per language c)
they are based on the simple intent and slot detection paradigm for
non-compositional queries. In this paper, we present a new multilingual
dataset, called MTOP, comprising of 100k annotated utterances in 6 languages
across 11 domains. We use this dataset and other publicly available datasets to
conduct a comprehensive benchmarking study on using various state-of-the-art
multilingual pre-trained models for task-oriented semantic parsing. We achieve
an average improvement of +6.3 points on Slot F1 for the two existing
multilingual datasets, over best results reported in their experiments.
Furthermore, we demonstrate strong zero-shot performance using pre-trained
models combined with automatic translation and alignment, and a proposed
distant supervision method to reduce the noise in slot label projection.
| 2,021 | Computation and Language |
Towards Improving Selective Prediction Ability of NLP Systems | It's better to say "I can't answer" than to answer incorrectly. This
selective prediction ability is crucial for NLP systems to be reliably deployed
in real-world applications. Prior work has shown that existing selective
prediction techniques fail to perform well, especially in the out-of-domain
setting. In this work, we propose a method that improves probability estimates
of models by calibrating them using prediction confidence and difficulty score
of instances. Using these two signals, we first annotate held-out instances and
then train a calibrator to predict the likelihood of correctness of the model's
prediction. We instantiate our method with Natural Language Inference (NLI) and
Duplicate Detection (DD) tasks and evaluate it in both In-Domain (IID) and
Out-of-Domain (OOD) settings. In (IID, OOD) settings, we show that the
representations learned by our calibrator result in an improvement of (15.81%,
5.64%) and (6.19%, 13.9%) over 'MaxProb' -- a selective prediction baseline --
on NLI and DD tasks respectively.
| 2,022 | Computation and Language |
EmoGraph: Capturing Emotion Correlations using Graph Networks | Most emotion recognition methods tackle the emotion understanding task by
considering individual emotion independently while ignoring their fuzziness
nature and the interconnections among them. In this paper, we explore how
emotion correlations can be captured and help different classification tasks.
We propose EmoGraph that captures the dependencies among different emotions
through graph networks. These graphs are constructed by leveraging the
co-occurrence statistics among different emotion categories. Empirical results
on two multi-label classification datasets demonstrate that EmoGraph
outperforms strong baselines, especially for macro-F1. An additional experiment
illustrates the captured emotion correlations can also benefit a single-label
classification task.
| 2,020 | Computation and Language |
A Variational Approach to Unsupervised Sentiment Analysis | In this paper, we propose a variational approach to unsupervised sentiment
analysis. Instead of using ground truth provided by domain experts, we use
target-opinion word pairs as a supervision signal. For example, in a document
snippet "the room is big," (room, big) is a target-opinion word pair. These
word pairs can be extracted by using dependency parsers and simple rules. Our
objective function is to predict an opinion word given a target word while our
ultimate goal is to learn a sentiment classifier. By introducing a latent
variable, i.e., the sentiment polarity, to the objective function, we can
inject the sentiment classifier to the objective function via the evidence
lower bound. We can learn a sentiment classifier by optimizing the lower bound.
We also impose sophisticated constraints on opinion words as regularization
which encourages that if two documents have similar (dissimilar) opinion words,
the sentiment classifiers should produce similar (different) probability
distribution. We apply our method to sentiment analysis on customer reviews and
clinical narratives. The experiment results show our method can outperform
unsupervised baselines in sentiment analysis task on both domains, and our
method obtains comparable results to the supervised method with hundreds of
labels per aspect in customer reviews domain, and obtains comparable results to
supervised methods in clinical narratives domain.
| 2,020 | Computation and Language |
Neural Machine Translation without Embeddings | Many NLP models operate over sequences of subword tokens produced by
hand-crafted tokenization rules and heuristic subword induction algorithms. A
simple universal alternative is to represent every computerized text as a
sequence of bytes via UTF-8, obviating the need for an embedding layer since
there are fewer token types (256) than dimensions. Surprisingly, replacing the
ubiquitous embedding layer with one-hot representations of each byte does not
hurt performance; experiments on byte-to-byte machine translation from English
to 10 different languages show a consistent improvement in BLEU, rivaling
character-level and even standard subword-level models. A deeper investigation
reveals that the combination of embeddingless models with decoder-input dropout
amounts to token dropout, which benefits byte-to-byte models in particular.
| 2,021 | Computation and Language |
Top2Vec: Distributed Representations of Topics | Topic modeling is used for discovering latent semantic structure, usually
referred to as topics, in a large collection of documents. The most widely used
methods are Latent Dirichlet Allocation and Probabilistic Latent Semantic
Analysis. Despite their popularity they have several weaknesses. In order to
achieve optimal results they often require the number of topics to be known,
custom stop-word lists, stemming, and lemmatization. Additionally these methods
rely on bag-of-words representation of documents which ignore the ordering and
semantics of words. Distributed representations of documents and words have
gained popularity due to their ability to capture semantics of words and
documents. We present $\texttt{top2vec}$, which leverages joint document and
word semantic embedding to find $\textit{topic vectors}$. This model does not
require stop-word lists, stemming or lemmatization, and it automatically finds
the number of topics. The resulting topic vectors are jointly embedded with the
document and word vectors with distance between them representing semantic
similarity. Our experiments demonstrate that $\texttt{top2vec}$ finds topics
which are significantly more informative and representative of the corpus
trained on than probabilistic generative models.
| 2,020 | Computation and Language |
Keywords lie far from the mean of all words in local vector space | Keyword extraction is an important document process that aims at finding a
small set of terms that concisely describe a document's topics. The most
popular state-of-the-art unsupervised approaches belong to the family of the
graph-based methods that build a graph-of-words and use various centrality
measures to score the nodes (candidate keywords). In this work, we follow a
different path to detect the keywords from a text document by modeling the main
distribution of the document's words using local word vector representations.
Then, we rank the candidates based on their position in the text and the
distance between the corresponding local vectors and the main distribution's
center. We confirm the high performance of our approach compared to strong
baselines and state-of-the-art unsupervised keyword extraction methods, through
an extended experimental study, investigating the properties of the local
representations.
| 2,020 | Computation and Language |
Howl: A Deployed, Open-Source Wake Word Detection System | We describe Howl, an open-source wake word detection toolkit with native
support for open speech datasets, like Mozilla Common Voice and Google Speech
Commands. We report benchmark results on Speech Commands and our own freely
available wake word detection dataset, built from MCV. We operationalize our
system for Firefox Voice, a plugin enabling speech interactivity for the
Firefox web browser. Howl represents, to the best of our knowledge, the first
fully productionized yet open-source wake word detection toolkit with a web
browser deployment target. Our codebase is at
https://github.com/castorini/howl.
| 2,020 | Computation and Language |
Abstractive Summarization of Spoken and Written Instructions with BERT | Summarization of speech is a difficult problem due to the spontaneity of the
flow, disfluencies, and other issues that are not usually encountered in
written texts. Our work presents the first application of the BERTSum model to
conversational language. We generate abstractive summaries of narrated
instructional videos across a wide variety of topics, from gardening and
cooking to software configuration and sports. In order to enrich the
vocabulary, we use transfer learning and pretrain the model on a few large
cross-domain datasets in both written and spoken English. We also do
preprocessing of transcripts to restore sentence segmentation and punctuation
in the output of an ASR system. The results are evaluated with ROUGE and
Content-F1 scoring for the How2 and WikiHow datasets. We engage human judges to
score a set of summaries randomly selected from a dataset curated from
HowTo100M and YouTube. Based on blind evaluation, we achieve a level of textual
fluency and utility close to that of summaries written by human content
creators. The model beats current SOTA when applied to WikiHow articles that
vary widely in style and topic, while showing no performance regression on the
canonical CNN/DailyMail dataset. Due to the high generalizability of the model
across different styles and domains, it has great potential to improve
accessibility and discoverability of internet content. We envision this
integrated as a feature in intelligent virtual assistants, enabling them to
summarize both written and spoken instructional content upon request.
| 2,020 | Computation and Language |
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and
Data Augmentation in Neural Models for Propaganda Detection in News Articles | This paper presents our systems for SemEval 2020 Shared Task 11: Detection of
Propaganda Techniques in News Articles. We participate in both the span
identification and technique classification subtasks and report on experiments
using different BERT-based models along with handcrafted features. Our models
perform well above the baselines for both tasks, and we contribute ablation
studies and discussion of our results to dissect the effectiveness of different
features and techniques with the goal of aiding future studies in propaganda
detection.
| 2,020 | Computation and Language |
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data
and Methodology | Conversational interfaces are increasingly popular as a way of connecting
people to information. Corpus-based conversational interfaces are able to
generate more diverse and natural responses than template-based or
retrieval-based agents. With their increased generative capacity of corpusbased
conversational agents comes the need to classify and filter out malevolent
responses that are inappropriate in terms of content and dialogue acts.
Previous studies on the topic of recognizing and classifying inappropriate
content are mostly focused on a certain category of malevolence or on single
sentences instead of an entire dialogue. In this paper, we define the task of
Malevolent Dialogue Response Detection and Classification (MDRDC). We make
three contributions to advance research on this task. First, we present a
Hierarchical Malevolent Dialogue Taxonomy (HMDT). Second, we create a labelled
multi-turn dialogue dataset and formulate the MDRDC task as a hierarchical
classification task over this taxonomy. Third, we apply stateof-the-art text
classification methods to the MDRDC task and report on extensive experiments
aimed at assessing the performance of these approaches.
| 2,020 | Computation and Language |
Applications of BERT Based Sequence Tagging Models on Chinese Medical
Text Attributes Extraction | We convert the Chinese medical text attributes extraction task into a
sequence tagging or machine reading comprehension task. Based on BERT
pre-trained models, we have not only tried the widely used LSTM-CRF sequence
tagging model, but also other sequence models, such as CNN, UCNN, WaveNet,
SelfAttention, etc, which reaches similar performance as LSTM+CRF. This sheds a
light on the traditional sequence tagging models. Since the aspect of emphasis
for different sequence tagging models varies substantially, ensembling these
models adds diversity to the final system. By doing so, our system achieves
good performance on the task of Chinese medical text attributes extraction
(subtask 2 of CCKS 2019 task 1).
| 2,020 | Computation and Language |
HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection | Sentiment analysis for code-mixed social media text continues to be an
under-explored area. This work adds two common approaches: fine-tuning large
transformer models and sample efficient methods like ULMFiT. Prior work
demonstrates the efficacy of classical ML methods for polarity detection.
Fine-tuned general-purpose language representation models, such as those of the
BERT family are benchmarked along with classical machine learning and ensemble
methods. We show that NB-SVM beats RoBERTa by 6.2% (relative) F1. The best
performing model is a majority-vote ensemble which achieves an F1 of 0.707. The
leaderboard submission was made under the codalab username nirantk, with F1 of
0.689.
| 2,020 | Computation and Language |
CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering
for Ensemble Models for Propaganda Detection | This paper describes our participation in the SemEval-2020 task Detection of
Propaganda Techniques in News Articles. We participate in both subtasks: Span
Identification (SI) and Technique Classification (TC). We use a bi-LSTM
architecture in the SI subtask and train a complex ensemble model for the TC
subtask. Our architectures are built using embeddings from BERT in combination
with additional lexical features and extensive label post-processing. Our
systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score:
43.86%) and 8 out of 31 teams in the TC subtask (F1-score: 57.37%).
| 2,020 | Computation and Language |
UTMN at SemEval-2020 Task 11: A Kitchen Solution to Automatic Propaganda
Detection | The article describes a fast solution to propaganda detection at SemEval-2020
Task 11, based onfeature adjustment. We use per-token vectorization of features
and a simple Logistic Regressionclassifier to quickly test different hypotheses
about our data. We come up with what seems to usthe best solution, however, we
are unable to align it with the result of the metric suggested by theorganizers
of the task. We test how our system handles class and feature imbalance by
varying thenumber of samples of two classes (Propaganda and None) in the
training set, the size of a contextwindow in which a token is vectorized and
combination of vectorization means. The result of oursystem at SemEval2020 Task
11 is F-score=0.37.
| 2,020 | Computation and Language |
DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda
Classification | This report describes the methods employed by the Democritus University of
Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of
Propaganda Techniques in News Articles. Our team dealt with Subtask 2:
Technique Classification. We used shallow Natural Language Processing (NLP)
preprocessing techniques to reduce the noise in the dataset, feature selection
methods, and common supervised machine learning algorithms. Our final model is
based on using the BERT system with entity mapping. To improve our model's
accuracy, we mapped certain words into five distinct categories by employing
word-classes and entity recognition.
| 2,020 | Computation and Language |
Quantum Language Model with Entanglement Embedding for Question
Answering | Quantum Language Models (QLMs) in which words are modelled as quantum
superposition of sememes have demonstrated a high level of model transparency
and good post-hoc interpretability. Nevertheless, in the current literature
word sequences are basically modelled as a classical mixture of word states,
which cannot fully exploit the potential of a quantum probabilistic
description. A full quantum model is yet to be developed to explicitly capture
the non-classical correlations within the word sequences. We propose a neural
network model with a novel Entanglement Embedding (EE) module, whose function
is to transform the word sequences into entangled pure states of many-body
quantum systems. Strong quantum entanglement, which is the central concept of
quantum information and an indication of parallelized correlations among the
words, is observed within the word sequences. Numerical experiments show that
the proposed QLM with EE (QLM-EE) achieves superior performance compared with
the classical deep neural network models and other QLMs on Question Answering
(QA) datasets. In addition, the post-hoc interpretability of the model can be
improved by quantizing the degree of entanglement among the words.
| 2,021 | Computation and Language |
An automated pipeline for the discovery of conspiracy and conspiracy
theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the
web | Although a great deal of attention has been paid to how conspiracy theories
circulate on social media and their factual counterpart conspiracies, there has
been little computational work done on describing their narrative structures.
We present an automated pipeline for the discovery and description of the
generative narrative frameworks of conspiracy theories on social media, and
actual conspiracies reported in the news media. We base this work on two
separate repositories of posts and news articles describing the well-known
conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy Bridgegate
from 2013. We formulate a graphical generative machine learning model where
nodes represent actors/actants, and multi-edges and self-loops among nodes
capture context-specific relationships. Posts and news items are viewed as
samples of subgraphs of the hidden narrative network. The problem of
reconstructing the underlying structure is posed as a latent model estimation
problem. We automatically extract and aggregate the actants and their
relationships from the posts and articles. We capture context specific actants
and interactant relationships by developing a system of supernodes and
subnodes. We use these to construct a network, which constitutes the underlying
narrative framework. We show how the Pizzagate framework relies on the
conspiracy theorists' interpretation of "hidden knowledge" to link otherwise
unlinked domains of human interaction, and hypothesize that this multi-domain
focus is an important feature of conspiracy theories. While Pizzagate relies on
the alignment of multiple domains, Bridgegate remains firmly rooted in the
single domain of New Jersey politics. We hypothesize that the narrative
framework of a conspiracy theory might stabilize quickly in contrast to the
narrative framework of an actual one, which may develop more slowly as
revelations come to light.
| 2,020 | Computation and Language |
COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural
Language Processing | The COVID-19 pandemic has affected people's lives in many ways. Social media
data can reveal public perceptions and experience with respect to the pandemic,
and also reveal factors that hamper or support efforts to curb global spread of
the disease. In this paper, we analyzed COVID-19-related comments collected
from six social media platforms using Natural Language Processing (NLP)
techniques. We identified relevant opinionated keyphrases and their respective
sentiment polarity (negative or positive) from over 1 million randomly selected
comments, and then categorized them into broader themes using thematic
analysis. Our results uncover 34 negative themes out of which 17 are economic,
socio-political, educational, and political issues. 20 positive themes were
also identified. We discuss the negative issues and suggest interventions to
tackle them based on the positive themes and research evidence.
| 2,022 | Computation and Language |
Deep Bayes Factor Scoring for Authorship Verification | The PAN 2020 authorship verification (AV) challenge focuses on a
cross-topic/closed-set AV task over a collection of fanfiction texts.
Fanfiction is a fan-written extension of a storyline in which a so-called
fandom topic describes the principal subject of the document. The data provided
in the PAN 2020 AV task is quite challenging because authors of texts across
multiple/different fandom topics are included. In this work, we present a
hierarchical fusion of two well-known approaches into a single end-to-end
learning procedure: A deep metric learning framework at the bottom aims to
learn a pseudo-metric that maps a document of variable length onto a
fixed-sized feature vector. At the top, we incorporate a probabilistic layer to
perform Bayes factor scoring in the learned metric space. We also provide text
preprocessing strategies to deal with the cross-topic issue.
| 2,020 | Computation and Language |
Predicting Helpfulness of Online Reviews | E-commerce dominates a large part of the world's economy with many websites
dedicated to online selling products. The vast majority of e-commerce websites
provide their customers with the ability to express their opinions about the
products/services they purchase. These feedback in the form of reviews
represent a rich source of information about the users' experiences and level
of satisfaction, which is of great benefit to both the producer and the
consumer. However, not all of these reviews are helpful/useful. The traditional
way of determining the helpfulness of a review is through the feedback from
human users. However, such a method does not necessarily cover all reviews.
Moreover, it has many issues like bias, high cost, etc. Thus, there is a need
to automate this process. This paper presents a set of machine learning (ML)
models to predict the helpfulness online reviews. Mainly, three approaches are
used: a supervised learning approach (using ML as well as deep learning (DL)
models), a semi-supervised approach (that combines DL models with word
embeddings), and pre-trained word embedding models that uses transfer learning
(TL). The latter two approaches are among the unique aspects of this paper as
they follow the recent trend of utilizing unlabeled text. The results show that
the proposed DL approaches have superiority over the traditional existing ones.
Moreover, the semi-supervised has a remarkable performance compared with the
other ones.
| 2,020 | Computation and Language |
syrapropa at SemEval-2020 Task 11: BERT-based Models Design For
Propagandistic Technique and Span Detection | This paper describes the BERT-based models proposed for two subtasks in
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. We
first build the model for Span Identification (SI) based on SpanBERT, and
facilitate the detection by a deeper model and a sentence-level representation.
We then develop a hybrid model for the Technique Classification (TC). The
hybrid model is composed of three submodels including two BERT models with
different training methods, and a feature-based Logistic Regression model. We
endeavor to deal with imbalanced dataset by adjusting cost function. We are in
the seventh place in SI subtask (0.4711 of F1-measure), and in the third place
in TC subtask (0.6783 of F1-measure) on the development set.
| 2,020 | Computation and Language |
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of
Propaganda Techniques in News Articles | This paper summarizes our studies on propaganda detection techniques for news
articles in the SemEval-2020 task 11. This task is divided into the SI and TC
subtasks. We implemented the GloVe word representation, the BERT pretraining
model, and the LSTM model architecture to accomplish this task. Our approach
achieved good results for both the SI and TC subtasks. The macro-F1-score for
the SI subtask is 0.406, and the micro-F1-score for the TC subtask is 0.505.
Our method significantly outperforms the officially released baseline method,
and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set.
This paper also compares the performances of different deep learning model
architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the
detection of news promotion techniques. The code of this paper is availabled
at: https://github.com/daojiaxu/semeval_11.
| 2,020 | Computation and Language |
Cross-lingual Semantic Role Labeling with Model Transfer | Prior studies show that cross-lingual semantic role labeling (SRL) can be
achieved by model transfer under the help of universal features. In this paper,
we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that
incorporates a variety of universal features and transfer methods. We study
both the bilingual transfer and multi-source transfer, under gold or
machine-generated syntactic inputs, pre-trained high-order abstract features,
and contextualized multilingual word representations. Experimental results on
the Universal Proposition Bank corpus indicate that performances of the
cross-lingual SRL can vary by leveraging different cross-lingual features. In
addition, whether the features are gold-standard also has an impact on
performances. Precisely, we find that gold syntax features are much more
crucial for cross-lingual SRL, compared with the automatically-generated ones.
Moreover, universal dependency structure features are able to give the best
help, and both pre-trained high-order features and contextualized word
representations can further bring significant improvements.
| 2,020 | Computation and Language |
Knowledge-Empowered Representation Learning for Chinese Medical Reading
Comprehension: Task, Model and Resources | Machine Reading Comprehension (MRC) aims to extract answers to questions
given a passage. It has been widely studied recently, especially in open
domains. However, few efforts have been made on closed-domain MRC, mainly due
to the lack of large-scale training data. In this paper, we introduce a
multi-target MRC task for the medical domain, whose goal is to predict answers
to medical questions and the corresponding support sentences from medical
information sources simultaneously, in order to ensure the high reliability of
medical knowledge serving. A high-quality dataset is manually constructed for
the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with
detailed analysis conducted. We further propose the Chinese medical BERT model
for the task (CMedBERT), which fuses medical knowledge into pre-trained
language models by the dynamic fusion mechanism of heterogeneous features and
the multi-task learning strategy. Experiments show that CMedBERT consistently
outperforms strong baselines by fusing context-aware and knowledge-aware token
representations.
| 2,021 | Computation and Language |
End to End Dialogue Transformer | Dialogue systems attempt to facilitate conversations between humans and
computers, for purposes as diverse as small talk to booking a vacation. We are
here inspired by the performance of the recurrent neural network-based model
Sequicity, which when conducting a dialogue uses a sequence-to-sequence
architecture to first produce a textual representation of what is going on in
the dialogue, and in a further step use this along with database findings to
produce a reply to the user. We here propose a dialogue system based on the
Transformer architecture instead of Sequicity's RNN-based architecture, that
works similarly in an end-to-end, sequence-to-sequence fashion.
| 2,020 | Computation and Language |
How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for
Token-level Evaluation Metrics | Though generative dialogue modeling is widely seen as a language modeling
task, the task demands an agent to have a complex natural language
understanding of its input text to carry a meaningful interaction with an user.
The automatic metrics used evaluate the quality of the generated text as a
proxy to the holistic interaction of the agent. Such metrics were earlier shown
to not correlate with the human judgement. In this work, we observe that human
evaluation of dialogue agents can be inconclusive due to the lack of sufficient
information for appropriate evaluation. The automatic metrics are deterministic
yet shallow and human evaluation can be relevant yet inconclusive. To bridge
this gap in evaluation, we propose designing a set of probing tasks to evaluate
dialogue models. The hand-crafted tasks are aimed at quantitatively evaluating
a generative dialogue model's understanding beyond the token-level evaluation
on the generated text. The probing tasks are deterministic like automatic
metrics and requires human judgement in their designing; benefiting from the
best of both worlds. With experiments on probe tasks we observe that, unlike
RNN based architectures, transformer model may not be learning to comprehend
the input text despite its generated text having higher overlap with the target
text.
| 2,020 | Computation and Language |
Prediction of ICD Codes with Clinical BERT Embeddings and Text
Augmentation with Label Balancing using MIMIC-III | This paper achieves state of the art results for the ICD code prediction task
using the MIMIC-III dataset. This was achieved through the use of Clinical BERT
(Alsentzer et al., 2019). embeddings and text augmentation and label balancing
to improve F1 scores for both ICD Chapter as well as ICD disease codes. We
attribute the improved performance mainly to the use of novel text augmentation
to shuffle the order of sentences during training. In comparison to the Top-32
ICD code prediction (Keyang Xu, et. al.) with an F1 score of 0.76, we achieve a
final F1 score of 0.75 but on a total of the top 50 ICD codes.
| 2,020 | Computation and Language |
Machine Semiotics | Recognizing a basic difference between the semiotics of humans and machines
presents a possibility to overcome the shortcomings of current speech assistive
devices. For the machine, the meaning of a (human) utterance is defined by its
own scope of actions. Machines, thus, do not need to understand the
conventional meaning of an utterance. Rather, they draw conversational
implicatures in the sense of (neo-)Gricean pragmatics. For speech assistive
devices, the learning of machine-specific meanings of human utterances, i.e.
the fossilization of conversational implicatures into conventionalized ones by
trial and error through lexicalization appears to be sufficient. Using the
quite trivial example of a cognitive heating device, we show that - based on
dynamic semantics - this process can be formalized as the reinforcement
learning of utterance-meaning pairs (UMP).
| 2,023 | Computation and Language |
Example-Based Named Entity Recognition | We present a novel approach to named entity recognition (NER) in the presence
of scarce data that we call example-based NER. Our train-free few-shot learning
approach takes inspiration from question-answering to identify entity spans in
a new and unseen domain. In comparison with the current state-of-the-art, the
proposed method performs significantly better, especially when using a low
number of support examples.
| 2,020 | Computation and Language |
A Baseline Analysis for Podcast Abstractive Summarization | Podcast summary, an important factor affecting end-users' listening
decisions, has often been considered a critical feature in podcast
recommendation systems, as well as many downstream applications. Existing
abstractive summarization approaches are mainly built on fine-tuned models on
professionally edited texts such as CNN and DailyMail news. Different from
news, podcasts are often longer, more colloquial and conversational, and
noisier with contents on commercials and sponsorship, which makes automatic
podcast summarization extremely challenging. This paper presents a baseline
analysis of podcast summarization using the Spotify Podcast Dataset provided by
TREC 2020. It aims to help researchers understand current state-of-the-art
pre-trained models and hence build a foundation for creating better models.
| 2,020 | Computation and Language |
Contextualized moral inference | Developing moral awareness in intelligent systems has shifted from a topic of
philosophical inquiry to a critical and practical issue in artificial
intelligence over the past decades. However, automated inference of everyday
moral situations remains an under-explored problem. We present a text-based
approach that predicts people's intuitive judgment of moral vignettes. Our
methodology builds on recent work in contextualized language models and textual
inference of moral sentiment. We show that a contextualized representation
offers a substantial advantage over alternative representations based on word
embeddings and emotion sentiment in inferring human moral judgment, evaluated
and reflected in three independent datasets from moral psychology. We discuss
the promise and limitations of our approach toward automated textual moral
reasoning.
| 2,020 | Computation and Language |
Conceptualized Representation Learning for Chinese Biomedical Text
Mining | Biomedical text mining is becoming increasingly important as the number of
biomedical documents and web data rapidly grows. Recently, word representation
models such as BERT has gained popularity among researchers. However, it is
difficult to estimate their performance on datasets containing biomedical texts
as the word distributions of general and biomedical corpora are quite
different. Moreover, the medical domain has long-tail concepts and
terminologies that are difficult to be learned via language models. For the
Chinese biomedical text, it is more difficult due to its complex structure and
the variety of phrase combinations. In this paper, we investigate how the
recently introduced pre-trained language model BERT can be adapted for Chinese
biomedical corpora and propose a novel conceptualized representation learning
approach. We also release a new Chinese Biomedical Language Understanding
Evaluation benchmark (\textbf{ChineseBLUE}). We examine the effectiveness of
Chinese pre-trained models: BERT, BERT-wwm, RoBERTa, and our approach.
Experimental results on the benchmark show that our approach could bring
significant gain. We release the pre-trained model on GitHub:
https://github.com/alibaba-research/ChineseBLUE.
| 2,023 | Computation and Language |
Simple Unsupervised Similarity-Based Aspect Extraction | In the context of sentiment analysis, there has been growing interest in
performing a finer granularity analysis focusing on the specific aspects of the
entities being evaluated. This is the goal of Aspect-Based Sentiment Analysis
(ABSA) which basically involves two tasks: aspect extraction and polarity
detection. The first task is responsible for discovering the aspects mentioned
in the review text and the second task assigns a sentiment orientation
(positive, negative, or neutral) to that aspect. Currently, the
state-of-the-art in ABSA consists of the application of deep learning methods
such as recurrent, convolutional and attention neural networks. The limitation
of these techniques is that they require a lot of training data and are
computationally expensive. In this paper, we propose a simple approach called
SUAEx for aspect extraction. SUAEx is unsupervised and relies solely on the
similarity of word embeddings. Experimental results on datasets from three
different domains have shown that SUAEx achieves results that can outperform
the state-of-the-art attention-based approach at a fraction of the time.
| 2,020 | Computation and Language |
TabSim: A Siamese Neural Network for Accurate Estimation of Table
Similarity | Tables are a popular and efficient means of presenting structured
information. They are used extensively in various kinds of documents including
web pages. Tables display information as a two-dimensional matrix, the
semantics of which is conveyed by a mixture of structure (rows, columns),
headers, caption, and content. Recent research has started to consider tables
as first class objects, not just as an addendum to texts, yielding interesting
results for problems like table matching, table completion, or value
imputation. All of these problems inherently rely on an accurate measure for
the semantic similarity of two tables. We present TabSim, a novel method to
compute table similarity scores using deep neural networks. Conceptually,
TabSim represents a table as a learned concatenation of embeddings of its
caption, its content, and its structure. Given two tables in this
representation, a Siamese neural network is trained to compute a score
correlating with the tables' semantic similarity. To train and evaluate our
method, we created a gold standard corpus consisting of 1500 table pairs
extracted from biomedical articles and manually scored regarding their degree
of similarity, and adopted two other corpora originally developed for a
different yet similar task. Our evaluation shows that TabSim outperforms other
table similarity measures on average by app. 7% pp F1-score in a binary
similarity classification setting and by app. 1.5% pp in a ranking scenario.
| 2,020 | Computation and Language |
Is this sentence valid? An Arabic Dataset for Commonsense Validation | The commonsense understanding and validation remains a challenging task in
the field of natural language understanding. Therefore, several research papers
have been published that studied the capability of proposed systems to evaluate
the models ability to validate commonsense in text. In this paper, we present a
benchmark Arabic dataset for commonsense understanding and validation as well
as a baseline research and models trained using the same dataset. To the best
of our knowledge, this dataset is considered as the first in the field of
Arabic text commonsense validation. The dataset is distributed under the
Creative Commons BY-SA 4.0 license and can be found on GitHub.
| 2,020 | Computation and Language |
ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation | Plug-and-play language models (PPLMs) enable topic-conditioned natural
language generation by pairing large pre-trained generators with attribute
models used to steer the predicted token distribution towards the selected
topic. Despite their computational efficiency, PPLMs require large amounts of
labeled texts to effectively balance generation fluency and proper
conditioning, making them unsuitable for low-resource settings. We present
ETC-NLG, an approach leveraging topic modeling annotations to enable
fully-unsupervised End-to-end Topic-Conditioned Natural Language Generation
over emergent topics in unlabeled document collections. We first test the
effectiveness of our approach in a low-resource setting for Italian, evaluating
the conditioning for both topic models and gold annotations. We then perform a
comparative evaluation of ETC-NLG for Italian and English using a parallel
corpus. Finally, we propose an automatic approach to estimate the effectiveness
of conditioning on the generated utterances.
| 2,020 | Computation and Language |
Query Understanding via Intent Description Generation | Query understanding is a fundamental problem in information retrieval (IR),
which has attracted continuous attention through the past decades. Many
different tasks have been proposed for understanding users' search queries,
e.g., query classification or query clustering. However, it is not that precise
to understand a search query at the intent class/cluster level due to the loss
of many detailed information. As we may find in many benchmark datasets, e.g.,
TREC and SemEval, queries are often associated with a detailed description
provided by human annotators which clearly describes its intent to help
evaluate the relevance of the documents. If a system could automatically
generate a detailed and precise intent description for a search query, like
human annotators, that would indicate much better query understanding has been
achieved. In this paper, therefore, we propose a novel
Query-to-Intent-Description (Q2ID) task for query understanding. Unlike those
existing ranking tasks which leverage the query and its description to compute
the relevance of documents, Q2ID is a reverse task which aims to generate a
natural language intent description based on both relevant and irrelevant
documents of a given query. To address this new task, we propose a novel
Contrastive Generation model, namely CtrsGen for short, to generate the intent
description by contrasting the relevant documents with the irrelevant documents
given a query. We demonstrate the effectiveness of our model by comparing with
several state-of-the-art generation models on the Q2ID task. We discuss the
potential usage of such Q2ID technique through an example application.
| 2,020 | Computation and Language |
Comparative Computational Analysis of Global Structure in Canonical,
Non-Canonical and Non-Literary Texts | This study investigates global properties of literary and non-literary texts.
Within the literary texts, a distinction is made between canonical and
non-canonical works. The central hypothesis of the study is that the three text
types (non-literary, literary/canonical and literary/non-canonical) exhibit
systematic differences with respect to structural design features as correlates
of aesthetic responses in readers. To investigate these differences, we
compiled a corpus containing texts of the three categories of interest, the
Jena Textual Aesthetics Corpus. Two aspects of global structure are
investigated, variability and self-similar (fractal) patterns, which reflect
long-range correlations along texts. We use four types of basic observations,
(i) the frequency of POS-tags per sentence, (ii) sentence length, (iii) lexical
diversity in chunks of text, and (iv) the distribution of topic probabilities
in chunks of texts. These basic observations are grouped into two more general
categories, (a) the low-level properties (i) and (ii), which are observed at
the level of the sentence (reflecting linguistic decoding), and (b) the
high-level properties (iii) and (iv), which are observed at the textual level
(reflecting comprehension). The basic observations are transformed into time
series, and these time series are subject to multifractal detrended fluctuation
analysis (MFDFA). Our results show that low-level properties of texts are
better discriminators than high-level properties, for the three text types
under analysis. Canonical literary texts differ from non-canonical ones
primarily in terms of variability. Fractality seems to be a universal feature
of text, more pronounced in non-literary than in literary texts. Beyond the
specific results of the study, we intend to open up new perspectives on the
experimental study of textual aesthetics.
| 2,021 | Computation and Language |
End-to-End Neural Transformer Based Spoken Language Understanding | Spoken language understanding (SLU) refers to the process of inferring the
semantic information from audio signals. While the neural transformers
consistently deliver the best performance among the state-of-the-art neural
architectures in field of natural language processing (NLP), their merits in a
closely related field, i.e., spoken language understanding (SLU) have not beed
investigated. In this paper, we introduce an end-to-end neural
transformer-based SLU model that can predict the variable-length domain,
intent, and slots vectors embedded in an audio signal with no intermediate
token prediction architecture. This new architecture leverages the
self-attention mechanism by which the audio signal is transformed to various
sub-subspaces allowing to extract the semantic context implied by an utterance.
Our end-to-end transformer SLU predicts the domains, intents and slots in the
Fluent Speech Commands dataset with accuracy equal to 98.1 \%, 99.6 \%, and
99.6 \%, respectively and outperforms the SLU models that leverage a
combination of recurrent and convolutional neural networks by 1.4 \% while the
size of our model is 25\% smaller than that of these architectures.
Additionally, due to independent sub-space projections in the self-attention
layer, the model is highly parallelizable which makes it a good candidate for
on-device SLU.
| 2,020 | Computation and Language |
JokeMeter at SemEval-2020 Task 7: Convolutional humor | This paper describes our system that was designed for Humor evaluation within
the SemEval-2020 Task 7. The system is based on convolutional neural network
architecture. We investigate the system on the official dataset, and we provide
more insight to model itself to see how the learned inner features look.
| 2,020 | Computation and Language |
Learning from students' perception on professors through opinion mining | Students' perception of classes measured through their opinions on teaching
surveys allows to identify deficiencies and problems, both in the environment
and in the learning methodologies. The purpose of this paper is to study,
through sentiment analysis using natural language processing (NLP) and machine
learning (ML) techniques, those opinions in order to identify topics that are
relevant for students, as well as predicting the associated sentiment via
polarity analysis. As a result, it is implemented, trained and tested two
algorithms to predict the associated sentiment as well as the relevant topics
of such opinions. The combination of both approaches then becomes useful to
identify specific properties of the students' opinions associated with each
sentiment label (positive, negative or neutral opinions) and topic.
Furthermore, we explore the possibility that students' perception surveys are
carried out without closed questions, relying on the information that students
can provide through open questions where they express their opinions about
their classes.
| 2,023 | Computation and Language |
A simple method for domain adaptation of sentence embeddings | Pre-trained sentence embeddings have been shown to be very useful for a
variety of NLP tasks. Due to the fact that training such embeddings requires a
large amount of data, they are commonly trained on a variety of text data. An
adaptation to specific domains could improve results in many cases, but such a
finetuning is usually problem-dependent and poses the risk of over-adapting to
the data used for adaptation. In this paper, we present a simple universal
method for finetuning Google's Universal Sentence Encoder (USE) using a Siamese
architecture. We demonstrate how to use this approach for a variety of data
sets and present results on different data sets representing similar problems.
The approach is also compared to traditional finetuning on these data sets. As
a further advantage, the approach can be used for combining data sets with
different annotations. We also present an embedding finetuned on all data sets
in parallel.
| 2,020 | Computation and Language |
The Impact of Indirect Machine Translation on Sentiment Classification | Sentiment classification has been crucial for many natural language
processing (NLP) applications, such as the analysis of movie reviews, tweets,
or customer feedback. A sufficiently large amount of data is required to build
a robust sentiment classification system. However, such resources are not
always available for all domains or for all languages.
In this work, we propose employing a machine translation (MT) system to
translate customer feedback into another language to investigate in which cases
translated sentences can have a positive or negative impact on an automatic
sentiment classifier. Furthermore, as performing a direct translation is not
always possible, we explore the performance of automatic classifiers on
sentences that have been translated using a pivot MT system.
We conduct several experiments using the above approaches to analyse the
performance of our proposed sentiment classification system and discuss the
advantages and drawbacks of classifying translated sentences.
| 2,020 | Computation and Language |
Extractive Summarizer for Scholarly Articles | We introduce an extractive method that will summarize long scientific papers.
Our model uses presentation slides provided by the authors of the papers as the
gold summary standard to label the sentences. The sentences are ranked based on
their novelty and their importance as estimated by deep neural networks. Our
window-based extractive labeling of sentences results in the improvement of at
least 4 ROUGE1-Recall points.
| 2,020 | Computation and Language |
Generating (Factual?) Narrative Summaries of RCTs: Experiments with
Neural Multi-Document Summarization | We consider the problem of automatically generating a narrative biomedical
evidence summary from multiple trial reports. We evaluate modern neural models
for abstractive summarization of relevant article abstracts from systematic
reviews previously conducted by members of the Cochrane collaboration, using
the authors conclusions section of the review abstract as our target. We enlist
medical professionals to evaluate generated summaries, and we find that modern
summarization systems yield consistently fluent and relevant synopses, but that
they are not always factual. We propose new approaches that capitalize on
domain-specific models to inform summarization, e.g., by explicitly demarcating
snippets of inputs that convey key findings, and emphasizing the reports of
large and high-quality trials. We find that these strategies modestly improve
the factual accuracy of generated summaries. Finally, we propose a new method
for automatically evaluating the factuality of generated narrative evidence
syntheses using models that infer the directionality of reported findings.
| 2,020 | Computation and Language |
Concept Extraction Using Pointer-Generator Networks | Concept extraction is crucial for a number of downstream applications.
However, surprisingly enough, straightforward single token/nominal
chunk-concept alignment or dictionary lookup techniques such as DBpedia
Spotlight still prevail. We propose a generic open-domain OOV-oriented
extractive model that is based on distant supervision of a pointer-generator
network leveraging bidirectional LSTMs and a copy mechanism. The model has been
trained on a large annotated corpus compiled specifically for this task from
250K Wikipedia pages, and tested on regular pages, where the pointers to other
pages are considered as ground truth concepts. The outcome of the experiments
shows that our model significantly outperforms standard techniques and, when
used on top of DBpedia Spotlight, further improves its performance. The
experiments furthermore show that the model can be readily ported to other
datasets on which it equally achieves a state-of-the-art performance.
| 2,020 | Computation and Language |
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for
Code-Mixed Social Media Text (Hinglish) | This paper discusses the design of the system used for providing a solution
for the problem given at SemEval-2020 Task 9 where sentiment analysis of
code-mixed language Hindi and English needed to be performed. This system uses
Weka as a tool for providing the classifier for the classification of tweets
and python is used for loading the data from the files provided and cleaning
it. Only part of the training data was provided to the system for classifying
the tweets in the test data set on which evaluation of the system was done. The
system performance was assessed using the official competition evaluation
metric F1-score. Classifier was trained on two sets of training data which
resulted in F1 scores of 0.4972 and 0.5316.
| 2,020 | Computation and Language |
Machine learning approach of Japanese composition scoring and writing
aided system's design | Automatic scoring system is extremely complex for any language. Because
natural language itself is a complex model. When we evaluate articles generated
by natural language, we need to view the articles from many dimensions such as
word features, grammatical features, semantic features, text structure and so
on. Even human beings sometimes can't accurately grade a composition because
different people have different opinions about the same article. But a
composition scoring system can greatly assist language learners. It can make
language leaner improve themselves in the process of output something. Though
it is still difficult for machines to directly evaluate a composition at the
semantic and pragmatic levels, especially for Japanese, Chinese and other
language in high context cultures, we can make machine evaluate a passage in
word and grammar levels, which can as an assistance of composition rater or
language learner. Especially for foreign language learners, lexical and
syntactic content are usually what they are more concerned about. In our
experiments, we did the follows works: 1) We use word segmentation tools and
dictionaries to achieve word segmentation of an article, and extract word
features, as well as generate a words' complexity feature of an article. And
Bow technique are used to extract the theme features. 2) We designed a
Turing-complete automata model and create 300+ automatons for the grammars that
appear in the JLPT examination. And extract grammars features by using these
automatons. 3) We propose a statistical approach for scoring a specify theme of
composition, the final score will depend on all the writings that submitted to
the system. 4) We design an grammar hint function for language leaner, so that
they can know currently what grammars they can use.
| 2,020 | Computation and Language |
Inno at SemEval-2020 Task 11: Leveraging Pure Transformer for
Multi-Class Propaganda Detection | The paper presents the solution of team "Inno" to a SEMEVAL 2020 task 11
"Detection of propaganda techniques in news articles". The goal of the second
subtask is to classify textual segments that correspond to one of the 18 given
propaganda techniques in news articles dataset. We tested a pure
Transformer-based model with an optimized learning scheme on the ability to
distinguish propaganda techniques between each other. Our model showed 0.6 and
0.58 overall F1 score on validation set and test set accordingly and non-zero
F1 score on each class on both sets.
| 2,020 | Computation and Language |
Analysis and Evaluation of Language Models for Word Sense Disambiguation | Transformer-based language models have taken many fields in NLP by storm.
BERT and its derivatives dominate most of the existing evaluation benchmarks,
including those for Word Sense Disambiguation (WSD), thanks to their ability in
capturing context-sensitive semantic nuances. However, there is still little
knowledge about their capabilities and potential limitations in encoding and
recovering word senses. In this article, we provide an in-depth quantitative
and qualitative analysis of the celebrated BERT model with respect to lexical
ambiguity. One of the main conclusions of our analysis is that BERT can
accurately capture high-level sense distinctions, even when a limited number of
examples is available for each word sense. Our analysis also reveals that in
some cases language models come close to solving coarse-grained noun
disambiguation under ideal conditions in terms of availability of training data
and computing resources. However, this scenario rarely occurs in real-world
settings and, hence, many practical challenges remain even in the
coarse-grained setting. We also perform an in-depth comparison of the two main
language model based WSD strategies, i.e., fine-tuning and feature extraction,
finding that the latter approach is more robust with respect to sense bias and
it can better exploit limited available training data. In fact, the simple
feature extraction strategy of averaging contextualized embeddings proves
robust even using only three training sentences per word sense, with minimal
improvements obtained by increasing the size of this training data.
| 2,021 | Computation and Language |
Discrete Word Embedding for Logical Natural Language Understanding | We propose an unsupervised neural model for learning a discrete embedding of
words. Unlike existing discrete embeddings, our binary embedding supports
vector arithmetic operations similar to continuous embeddings. Our embedding
represents each word as a set of propositional statements describing a
transition rule in classical/STRIPS planning formalism. This makes the
embedding directly compatible with symbolic, state of the art classical
planning solvers.
| 2,020 | Computation and Language |
SHAP values for Explaining CNN-based Text Classification Models | Deep neural networks are increasingly used in natural language processing
(NLP) models. However, the need to interpret and explain the results from
complex algorithms are limiting their widespread adoption in regulated
industries such as banking. There has been recent work on interpretability of
machine learning algorithms with structured data. But there are only limited
techniques for NLP applications where the problem is more challenging due to
the size of the vocabulary, high-dimensional nature, and the need to consider
textual coherence and language structure. This paper develops a methodology to
compute SHAP values for local explainability of CNN-based text classification
models. The approach is also extended to compute global scores to assess the
importance of features. The results are illustrated on sentiment analysis of
Amazon Electronic Review data.
| 2,021 | Computation and Language |
On the Optimality of Vagueness: "Around", "Between", and the Gricean
Maxims | Why is ordinary language vague? We argue that in contexts in which a
cooperative speaker is not perfectly informed about the world, the use of vague
expressions can offer an optimal tradeoff between truthfulness (Gricean
Quality) and informativeness (Gricean Quantity). Focusing on expressions of
approximation such as "around", which are semantically vague, we show that they
allow the speaker to convey indirect probabilistic information, in a way that
can give the listener a more accurate representation of the information
available to the speaker than any more precise expression would (intervals of
the form "between"). That is, vague sentences can be more informative than
their precise counterparts. We give a probabilistic treatment of the
interpretation of "around", and offer a model for the interpretation and use of
"around"-statements within the Rational Speech Act (RSA) framework. In our
account the shape of the speaker's distribution matters in ways not predicted
by the Lexical Uncertainty model standardly used in the RSA framework for vague
predicates. We use our approach to draw further lessons concerning the semantic
flexibility of vague expressions and their irreducibility to more precise
meanings.
| 2,022 | Computation and Language |
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization | Pre-trained language models such as BERT have exhibited remarkable
performances in many tasks in natural language understanding (NLU). The tokens
in the models are usually fine-grained in the sense that for languages like
English they are words or sub-words and for languages like Chinese they are
characters. In English, for example, there are multi-word expressions which
form natural lexical units and thus the use of coarse-grained tokenization also
appears to be reasonable. In fact, both fine-grained and coarse-grained
tokenizations have advantages and disadvantages for learning of pre-trained
language models. In this paper, we propose a novel pre-trained language model,
referred to as AMBERT (A Multi-grained BERT), on the basis of both fine-grained
and coarse-grained tokenizations. For English, AMBERT takes both the sequence
of words (fine-grained tokens) and the sequence of phrases (coarse-grained
tokens) as input after tokenization, employs one encoder for processing the
sequence of words and the other encoder for processing the sequence of the
phrases, utilizes shared parameters between the two encoders, and finally
creates a sequence of contextualized representations of the words and a
sequence of contextualized representations of the phrases. Experiments have
been conducted on benchmark datasets for Chinese and English, including CLUE,
GLUE, SQuAD and RACE. The results show that AMBERT can outperform BERT in all
cases, particularly the improvements are significant for Chinese. We also
develop a method to improve the efficiency of AMBERT in inference, which still
performs better than BERT with the same computational cost as BERT.
| 2,021 | Computation and Language |
Automatic Speech Summarisation: A Scoping Review | Speech summarisation techniques take human speech as input and then output an
abridged version as text or speech. Speech summarisation has applications in
many domains from information technology to health care, for example improving
speech archives or reducing clinical documentation burden. This scoping review
maps the speech summarisation literature, with no restrictions on time frame,
language summarised, research method, or paper type. We reviewed a total of 110
papers out of a set of 153 found through a literature search and extracted
speech features used, methods, scope, and training corpora. Most studies employ
one of four speech summarisation architectures: (1) Sentence extraction and
compaction; (2) Feature extraction and classification or rank-based sentence
selection; (3) Sentence compression and compression summarisation; and (4)
Language modelling. We also discuss the strengths and weaknesses of these
different methods and speech features. Overall, supervised methods (e.g. Hidden
Markov support vector machines, Ranking support vector machines, Conditional
random fields) performed better than unsupervised methods. As supervised
methods require manually annotated training data which can be costly, there was
more interest in unsupervised methods. Recent research into unsupervised
methods focusses on extending language modelling, for example by combining
Uni-gram modelling with deep neural networks. Protocol registration: The
protocol for this scoping review is registered at https://osf.io.
| 2,020 | Computation and Language |
Relation/Entity-Centric Reading Comprehension | Constructing a machine that understands human language is one of the most
elusive and long-standing challenges in artificial intelligence. This thesis
addresses this challenge through studies of reading comprehension with a focus
on understanding entities and their relationships. More specifically, we focus
on question answering tasks designed to measure reading comprehension. We focus
on entities and relations because they are typically used to represent the
semantics of natural language.
| 2,020 | Computation and Language |
Improvement of a dedicated model for open domain persona-aware dialogue
generation | This paper analyzes some speed and performance improvement methods of
Transformer architecture in recent years, mainly its application in dedicated
model training. The dedicated model studied here refers to the open domain
persona-aware dialogue generation model, and the dataset is multi turn short
dialogue, The total length of a single input sequence is no more than 105
tokens. Therefore, many improvements in the architecture and attention
mechanism of transformer architecture for long sequence processing are not
discussed in this paper. The source code of the experiments has been open
sourced: https://github.com/ghosthamlet/persona
| 2,020 | Computation and Language |
Opinion-aware Answer Generation for Review-driven Question Answering in
E-Commerce | Product-related question answering (QA) is an important but challenging task
in E-Commerce. It leads to a great demand on automatic review-driven QA, which
aims at providing instant responses towards user-posted questions based on
diverse product reviews. Nevertheless, the rich information about personal
opinions in product reviews, which is essential to answer those
product-specific questions, is underutilized in current generation-based
review-driven QA studies. There are two main challenges when exploiting the
opinion information from the reviews to facilitate the opinion-aware answer
generation: (i) jointly modeling opinionated and interrelated information
between the question and reviews to capture important information for answer
generation, (ii) aggregating diverse opinion information to uncover the common
opinion towards the given question. In this paper, we tackle opinion-aware
answer generation by jointly learning answer generation and opinion mining
tasks with a unified model. Two kinds of opinion fusion strategies, namely,
static and dynamic fusion, are proposed to distill and aggregate important
opinion information learned from the opinion mining task into the answer
generation process. Then a multi-view pointer-generator network is employed to
generate opinion-aware answers for a given product-related question.
Experimental results show that our method achieves superior performance in
real-world E-Commerce QA datasets, and effectively generate opinionated and
informative answers.
| 2,020 | Computation and Language |
Query Focused Multi-document Summarisation of Biomedical Texts | This paper presents the participation of Macquarie University and the
Australian National University for Task B Phase B of the 2020 BioASQ Challenge
(BioASQ8b). Our overall framework implements Query focused multi-document
extractive summarisation by applying either a classification or a regression
layer to the candidate sentence embeddings and to the comparison between the
question and sentence embeddings. We experiment with variants using BERT and
BioBERT, Siamese architectures, and reinforcement learning. We observe the best
results when BERT is used to obtain the word embeddings, followed by an LSTM
layer to obtain sentence embeddings. Variants using Siamese architectures or
BioBERT did not improve the results.
| 2,020 | Computation and Language |
A Survey of Evaluation Metrics Used for NLG Systems | The success of Deep Learning has created a surge in interest in a wide a
range of Natural Language Generation (NLG) tasks. Deep Learning has not only
pushed the state of the art in several existing NLG tasks but has also
facilitated researchers to explore various newer NLG tasks such as image
captioning. Such rapid progress in NLG has necessitated the development of
accurate automatic evaluation metrics that would allow us to track the progress
in the field of NLG. However, unlike classification tasks, automatically
evaluating NLG systems in itself is a huge challenge. Several works have shown
that early heuristic-based metrics such as BLEU, ROUGE are inadequate for
capturing the nuances in the different NLG tasks. The expanding number of NLG
models and the shortcomings of the current metrics has led to a rapid surge in
the number of evaluation metrics proposed since 2014. Moreover, various
evaluation metrics have shifted from using pre-determined heuristic-based
formulae to trained transformer models. This rapid change in a relatively short
time has led to the need for a survey of the existing NLG metrics to help
existing and new researchers to quickly come up to speed with the developments
that have happened in NLG evaluation in the last few years. Through this
survey, we first wish to highlight the challenges and difficulties in
automatically evaluating NLG systems. Then, we provide a coherent taxonomy of
the evaluation metrics to organize the existing metrics and to better
understand the developments in the field. We also describe the different
metrics in detail and highlight their key contributions. Later, we discuss the
main shortcomings identified in the existing metrics and describe the
methodology used to evaluate evaluation metrics. Finally, we discuss our
suggestions and recommendations on the next steps forward to improve the
automatic evaluation metrics.
| 2,020 | Computation and Language |
GREEK-BERT: The Greeks visiting Sesame Street | Transformer-based language models, such as BERT and its variants, have
achieved state-of-the-art performance in several downstream natural language
processing (NLP) tasks on generic benchmark datasets (e.g., GLUE, SQUAD, RACE).
However, these models have mostly been applied to the resource-rich English
language. In this paper, we present GREEK-BERT, a monolingual BERT-based
language model for modern Greek. We evaluate its performance in three NLP
tasks, i.e., part-of-speech tagging, named entity recognition, and natural
language inference, obtaining state-of-the-art performance. Interestingly, in
two of the benchmarks GREEK-BERT outperforms two multilingual Transformer-based
models (M-BERT, XLM-R), as well as shallower neural baselines operating on
pre-trained word embeddings, by a large margin (5%-10%). Most importantly, we
make both GREEK-BERT and our training code publicly available, along with code
illustrating how GREEK-BERT can be fine-tuned for downstream NLP tasks. We
expect these resources to boost NLP research and applications for modern Greek.
| 2,020 | Computation and Language |
Uralic Language Identification (ULI) 2020 shared task dataset and the
Wanca 2017 corpus | This article introduces the Wanca 2017 corpus of texts crawled from the
internet from which the sentences in rare Uralic languages for the use of the
Uralic Language Identification (ULI) 2020 shared task were collected. We
describe the ULI dataset and how it was constructed using the Wanca 2017 corpus
and texts in different languages from the Leipzig corpora collection. We also
provide baseline language identification experiments conducted using the ULI
2020 dataset.
| 2,020 | Computation and Language |
Domain-shift Conditioning using Adaptable Filtering via Hierarchical
Embeddings for Robust Chinese Spell Check | Spell check is a useful application which processes noisy human-generated
text. Spell check for Chinese poses unresolved problems due to the large number
of characters, the sparse distribution of errors, and the dearth of resources
with sufficient coverage of heterogeneous and shifting error domains. For
Chinese spell check, filtering using confusion sets narrows the search space
and makes finding corrections easier. However, most, if not all, confusion sets
used to date are fixed and thus do not include new, shifting error domains. We
propose a scalable adaptable filter that exploits hierarchical character
embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve
sparsity problems related to infrequent errors. Our approach compares favorably
with competitive baselines and obtains SOTA results on the 2014 and 2015
Chinese Spelling Check Bake-off datasets.
| 2,021 | Computation and Language |
Entity and Evidence Guided Relation Extraction for DocRED | Document-level relation extraction is a challenging task which requires
reasoning over multiple sentences in order to predict relations in a document.
In this paper, we pro-pose a joint training frameworkE2GRE(Entity and Evidence
Guided Relation Extraction)for this task. First, we introduce entity-guided
sequences as inputs to a pre-trained language model (e.g. BERT, RoBERTa). These
entity-guided sequences help a pre-trained language model (LM) to focus on
areas of the document related to the entity. Secondly, we guide the fine-tuning
of the pre-trained language model by using its internal attention probabilities
as additional features for evidence prediction.Our new approach encourages the
pre-trained language model to focus on the entities and supporting/evidence
sentences. We evaluate our E2GRE approach on DocRED, a recently released
large-scale dataset for relation extraction. Our approach is able to achieve
state-of-the-art results on the public leaderboard across all metrics, showing
that our E2GRE is both effective and synergistic on relation extraction and
evidence prediction.
| 2,020 | Computation and Language |
Neural Generation Meets Real People: Towards Emotionally Engaging
Mixed-Initiative Conversations | We present Chirpy Cardinal, an open-domain dialogue agent, as a research
platform for the 2019 Alexa Prize competition. Building an open-domain
socialbot that talks to real people is challenging - such a system must meet
multiple user expectations such as broad world knowledge, conversational style,
and emotional connection. Our socialbot engages users on their terms -
prioritizing their interests, feelings and autonomy. As a result, our socialbot
provides a responsive, personalized user experience, capable of talking
knowledgeably about a wide variety of topics, as well as chatting
empathetically about ordinary life. Neural generation plays a key role in
achieving these goals, providing the backbone for our conversational and
emotional tone. At the end of the competition, Chirpy Cardinal progressed to
the finals with an average rating of 3.6/5.0, a median conversation duration of
2 minutes 16 seconds, and a 90th percentile duration of over 12 minutes.
| 2,020 | Computation and Language |
Repurposing TREC-COVID Annotations to Answer the Key Questions of
CORD-19 | The novel coronavirus disease 2019 (COVID-19) began in Wuhan, China in late
2019 and to date has infected over 14M people worldwide, resulting in over
750,000 deaths. On March 10, 2020 the World Health Organization (WHO) declared
the outbreak a global pandemic. Many academics and researchers, not restricted
to the medical domain, began publishing papers describing new discoveries.
However, with the large influx of publications, it was hard for these
individuals to sift through the large amount of data and make sense of the
findings. The White House and a group of industry research labs, lead by the
Allen Institute for AI, aggregated over 200,000 journal articles related to a
variety of coronaviruses and tasked the community with answering key questions
related to the corpus, releasing the dataset as CORD-19. The information
retrieval (IR) community repurposed the journal articles within CORD-19 to more
closely resemble a classic TREC-style competition, dubbed TREC-COVID, with
human annotators providing relevancy judgements at the end of each round of
competition. Seeing the related endeavors, we set out to repurpose the
relevancy annotations for TREC-COVID tasks to identify journal articles in
CORD-19 which are relevant to the key questions posed by CORD-19. A BioBERT
model trained on this repurposed dataset prescribes relevancy annotations for
CORD-19 tasks that have an overall agreement of 0.4430 with majority human
annotations in terms of Cohen's kappa. We present the methodology used to
construct the new dataset and describe the decision process used throughout.
| 2,020 | Computation and Language |
Language Models as Emotional Classifiers for Textual Conversations | Emotions play a critical role in our everyday lives by altering how we
perceive, process and respond to our environment. Affective computing aims to
instill in computers the ability to detect and act on the emotions of human
actors. A core aspect of any affective computing system is the classification
of a user's emotion. In this study we present a novel methodology for
classifying emotion in a conversation. At the backbone of our proposed
methodology is a pre-trained Language Model (LM), which is supplemented by a
Graph Convolutional Network (GCN) that propagates information over the
predicate-argument structure identified in an utterance. We apply our proposed
methodology on the IEMOCAP and Friends data sets, achieving state-of-the-art
performance on the former and a higher accuracy on certain emotional labels on
the latter. Furthermore, we examine the role context plays in our methodology
by altering how much of the preceding conversation the model has access to when
making a classification.
| 2,020 | Computation and Language |
QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content
Identification in Hindi Language | We describe our top-team solution to Task 1 for Hindi in the HASOC contest
organised by FIRE 2019. The task is to identify hate speech and offensive
language in Hindi. More specifically, it is a binary classification problem
where a system is required to classify tweets into two classes: (a) \emph{Hate
and Offensive (HOF)} and (b) \emph{Not Hate or Offensive (NOT)}. In contrast to
the popular idea of pretraining word vectors (a.k.a. word embedding) with a
large corpus from a general domain such as Wikipedia, we used a relatively
small collection of relevant tweets (i.e. random and sarcasm tweets in Hindi
and Hinglish) for pretraining. We trained a Convolutional Neural Network (CNN)
on top of the pretrained word vectors. This approach allowed us to be ranked
first for this task out of all teams. Our approach could easily be adapted to
other applications where the goal is to predict class of a text when the
provided context is limited.
| 2,019 | Computation and Language |
Misogynistic Tweet Detection: Modelling CNN with Small Datasets | Online abuse directed towards women on the social media platform Twitter has
attracted considerable attention in recent years. An automated method to
effectively identify misogynistic abuse could improve our understanding of the
patterns, driving factors, and effectiveness of responses associated with
abusive tweets over a sustained time period. However, training a neural network
(NN) model with a small set of labelled data to detect misogynistic tweets is
difficult. This is partly due to the complex nature of tweets which contain
misogynistic content, and the vast number of parameters needed to be learned in
a NN model. We have conducted a series of experiments to investigate how to
train a NN model to detect misogynistic tweets effectively. In particular, we
have customised and regularised a Convolutional Neural Network (CNN)
architecture and shown that the word vectors pre-trained on a task-specific
domain can be used to train a CNN model effectively when a small set of
labelled data is available. A CNN model trained in this way yields an improved
accuracy over the state-of-the-art models.
| 2,018 | Computation and Language |
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity
Recognition | Clinical Named Entity Recognition (CNER) aims to automatically identity
clinical terminologies in Electronic Health Records (EHRs), which is a
fundamental and crucial step for clinical research. To train a high-performance
model for CNER, it usually requires a large number of EHRs with high-quality
labels. However, labeling EHRs, especially Chinese EHRs, is time-consuming and
expensive. One effective solution to this is active learning, where a model
asks labelers to annotate data which the model is uncertain of. Conventional
active learning assumes a single labeler that always replies noiseless answers
to queried labels. However, in real settings, multiple labelers provide diverse
quality of annotation with varied costs and labelers with low overall
annotation quality can still assign correct labels for some specific instances.
In this paper, we propose a Cost-Quality Adaptive Active Learning (CQAAL)
approach for CNER in Chinese EHRs, which maintains a balance between the
annotation quality, labeling costs, and the informativeness of selected
instances. Specifically, CQAAL selects cost-effective instance-labeler pairs to
achieve better annotation quality with lower costs in an adaptive manner.
Computational results on the CCKS-2017 Task 2 benchmark dataset demonstrate the
superiority and effectiveness of the proposed CQAAL.
| 2,020 | Computation and Language |
The Adapter-Bot: All-In-One Controllable Conversational Model | Considerable progress has been made towards conversational models that
generate coherent and fluent responses by training large language models on
large dialogue datasets. These models have little or no control of the
generated responses and miss two important features: continuous dialogue skills
integration and seamlessly leveraging diverse knowledge sources. In this paper,
we propose the Adapter-Bot, a dialogue model that uses a fixed backbone
conversational model such as DialGPT (Zhang et al., 2019) and triggers
on-demand dialogue skills (e.g., emphatic response, weather information, movie
recommendation) via different adapters (Houlsby et al., 2019). Each adapter can
be trained independently, thus allowing a continual integration of skills
without retraining the entire model. Depending on the skills, the model is able
to process multiple knowledge types, such as text, tables, and graphs, in a
seamless manner. The dialogue skills can be triggered automatically via a
dialogue manager, or manually, thus allowing high-level control of the
generated responses. At the current stage, we have implemented 12 response
styles (e.g., positive, negative etc.), 8 goal-oriented skills (e.g. weather
information, movie recommendation, etc.), and personalized and emphatic
responses. We evaluate our model using automatic evaluation by comparing it
with existing state-of-the-art conversational models, and we have released an
interactive system at adapter.bot.ust.hk.
| 2,020 | Computation and Language |
Two Step Joint Model for Drug Drug Interaction Extraction | When patients need to take medicine, particularly taking more than one kind
of drug simultaneously, they should be alarmed that there possibly exists
drug-drug interaction. Interaction between drugs may have a negative impact on
patients or even cause death. Generally, drugs that conflict with a specific
drug (or label drug) are usually described in its drug label or package insert.
Since more and more new drug products come into the market, it is difficult to
collect such information by manual. We take part in the Drug-Drug Interaction
(DDI) Extraction from Drug Labels challenge of Text Analysis Conference (TAC)
2018, choosing task1 and task2 to automatically extract DDI related mentions
and DDI relations respectively. Instead of regarding task1 as named entity
recognition (NER) task and regarding task2 as relation extraction (RE) task
then solving it in a pipeline, we propose a two step joint model to detect DDI
and it's related mentions jointly. A sequence tagging system (CNN-GRU
encoder-decoder) finds precipitants first and search its fine-grained Trigger
and determine the DDI for each precipitant in the second step. Moreover, a rule
based model is built to determine the sub-type for pharmacokinetic interation.
Our system achieved best result in both task1 and task2. F-measure reaches 0.46
in task1 and 0.40 in task2.
| 2,020 | Computation and Language |
Linked Credibility Reviews for Explainable Misinformation Detection | In recent years, misinformation on the Web has become increasingly rampant.
The research community has responded by proposing systems and challenges, which
are beginning to be useful for (various subtasks of) detecting misinformation.
However, most proposed systems are based on deep learning techniques which are
fine-tuned to specific domains, are difficult to interpret and produce results
which are not machine readable. This limits their applicability and adoption as
they can only be used by a select expert audience in very specific settings. In
this paper we propose an architecture based on a core concept of Credibility
Reviews (CRs) that can be used to build networks of distributed bots that
collaborate for misinformation detection. The CRs serve as building blocks to
compose graphs of (i) web content, (ii) existing credibility signals
--fact-checked claims and reputation reviews of websites--, and (iii)
automatically computed reviews. We implement this architecture on top of
lightweight extensions to Schema.org and services providing generic NLP tasks
for semantic similarity and stance detection. Evaluations on existing datasets
of social-media posts, fake news and political speeches demonstrates several
advantages over existing systems: extensibility, domain-independence,
composability, explainability and transparency via provenance. Furthermore, we
obtain competitive results without requiring finetuning and establish a new
state of the art on the Clef'18 CheckThat! Factuality task.
| 2,020 | Computation and Language |
Rethinking the Objectives of Extractive Question Answering | This work demonstrates that using the objective with independence assumption
for modelling the span probability $P(a_s,a_e) = P(a_s)P(a_e)$ of span starting
at position $a_s$ and ending at position $a_e$ has adverse effects. Therefore
we propose multiple approaches to modelling joint probability $P(a_s,a_e)$
directly. Among those, we propose a compound objective, composed from the joint
probability while still keeping the objective with independence assumption as
an auxiliary objective. We find that the compound objective is consistently
superior or equal to other assumptions in exact match. Additionally, we
identified common errors caused by the assumption of independence and manually
checked the counterpart predictions, demonstrating the impact of the compound
objective on the real examples. Our findings are supported via experiments with
three extractive QA models (BIDAF, BERT, ALBERT) over six datasets and our
code, individual results and manual analysis are available online.
| 2,021 | Computation and Language |
HittER: Hierarchical Transformers for Knowledge Graph Embeddings | This paper examines the challenging problem of learning representations of
entities and relations in a complex multi-relational knowledge graph. We
propose HittER, a Hierarchical Transformer model to jointly learn
Entity-relation composition and Relational contextualization based on a source
entity's neighborhood. Our proposed model consists of two different Transformer
blocks: the bottom block extracts features of each entity-relation pair in the
local neighborhood of the source entity and the top block aggregates the
relational information from outputs of the bottom block. We further design a
masked entity prediction task to balance information from the relational
context and the source entity itself. Experimental results show that HittER
achieves new state-of-the-art results on multiple link prediction datasets. We
additionally propose a simple approach to integrate HittER into BERT and
demonstrate its effectiveness on two Freebase factoid question answering
datasets.
| 2,021 | Computation and Language |
HeteGCN: Heterogeneous Graph Convolutional Networks for Text
Classification | We consider the problem of learning efficient and inductive graph
convolutional networks for text classification with a large number of examples
and features. Existing state-of-the-art graph embedding based methods such as
predictive text embedding (PTE) and TextGCN have shortcomings in terms of
predictive performance, scalability and inductive capability. To address these
limitations, we propose a heterogeneous graph convolutional network (HeteGCN)
modeling approach that unites the best aspects of PTE and TextGCN together. The
main idea is to learn feature embeddings and derive document embeddings using a
HeteGCN architecture with different graphs used across layers. We simplify
TextGCN by dissecting into several HeteGCN models which (a) helps to study the
usefulness of individual models and (b) offers flexibility in fusing learned
embeddings from different models. In effect, the number of model parameters is
reduced significantly, enabling faster training and improving performance in
small labeled training set scenario. Our detailed experimental studies
demonstrate the efficacy of the proposed approach.
| 2,020 | Computation and Language |
TATL at W-NUT 2020 Task 2: A Transformer-based Baseline System for
Identification of Informative COVID-19 English Tweets | As the COVID-19 outbreak continues to spread throughout the world, more and
more information about the pandemic has been shared publicly on social media.
For example, there are a huge number of COVID-19 English Tweets daily on
Twitter. However, the majority of those Tweets are uninformative, and hence it
is important to be able to automatically select only the informative ones for
downstream applications. In this short paper, we present our participation in
the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 English
Tweets. Inspired by the recent advances in pretrained Transformer language
models, we propose a simple yet effective baseline for the task. Despite its
simplicity, our proposed approach shows very competitive results in the
leaderboard as we ranked 8 over 56 teams participated in total.
| 2,020 | Computation and Language |
Knowledge Efficient Deep Learning for Natural Language Processing | Deep learning has become the workhorse for a wide range of natural language
processing applications. But much of the success of deep learning relies on
annotated examples. Annotation is time-consuming and expensive to produce at
scale. Here we are interested in methods for reducing the required quantity of
annotated data -- by making the learning methods more knowledge efficient so as
to make them more applicable in low annotation (low resource) settings. There
are various classical approaches to making the models more knowledge efficient
such as multi-task learning, transfer learning, weakly supervised and
unsupervised learning etc. This thesis focuses on adapting such classical
methods to modern deep learning models and algorithms.
This thesis describes four works aimed at making machine learning models more
knowledge efficient. First, we propose a knowledge rich deep learning model
(KRDL) as a unifying learning framework for incorporating prior knowledge into
deep models. In particular, we apply KRDL built on Markov logic networks to
denoise weak supervision. Second, we apply a KRDL model to assist the machine
reading models to find the correct evidence sentences that can support their
decision. Third, we investigate the knowledge transfer techniques in
multilingual setting, where we proposed a method that can improve pre-trained
multilingual BERT based on the bilingual dictionary. Fourth, we present an
episodic memory network for language modelling, in which we encode the large
external knowledge for the pre-trained GPT.
| 2,020 | Computation and Language |
Zero-Resource Knowledge-Grounded Dialogue Generation | While neural conversation models have shown great potentials towards
generating informative and engaging responses via introducing external
knowledge, learning such a model often requires knowledge-grounded dialogues
that are difficult to obtain. To overcome the data challenge and reduce the
cost of building a knowledge-grounded dialogue system, we explore the problem
under a zero-resource setting by assuming no context-knowledge-response triples
are needed for training. To this end, we propose representing the knowledge
that bridges a context and a response and the way that the knowledge is
expressed as latent variables, and devise a variational approach that can
effectively estimate a generation model from a dialogue corpus and a knowledge
corpus that are independent with each other. Evaluation results on three
benchmarks of knowledge-grounded dialogue generation indicate that our model
can achieve comparable performance with state-of-the-art methods that rely on
knowledge-grounded dialogues for training, and exhibits a good generalization
ability over different topics and different datasets.
| 2,021 | Computation and Language |
Efficient Computation of Expectations under Spanning Tree Distributions | We give a general framework for inference in spanning tree models. We propose
unified algorithms for the important cases of first-order expectations and
second-order expectations in edge-factored, non-projective spanning-tree
models. Our algorithms exploit a fundamental connection between gradients and
expectations, which allows us to derive efficient algorithms. These algorithms
are easy to implement with or without automatic differentiation software. We
motivate the development of our framework with several \emph{cautionary tales}
of previous research, which has developed numerous inefficient algorithms for
computing expectations and their gradients. We demonstrate how our framework
efficiently computes several quantities with known algorithms, including the
expected attachment score, entropy, and generalized expectation criteria. As a
bonus, we give algorithms for quantities that are missing in the literature,
including the KL divergence. In all cases, our approach matches the efficiency
of existing algorithms and, in several cases, reduces the runtime complexity by
a factor of the sentence length. We validate the implementation of our
framework through runtime experiments. We find our algorithms are up to 15 and
9 times faster than previous algorithms for computing the Shannon entropy and
the gradient of the generalized expectation objective, respectively.
| 2,021 | Computation and Language |
SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting
Propaganda using Sentence-Level Emotional Salience Features | This paper describes a system developed for detecting propaganda techniques
from news articles. We focus on examining how emotional salience features
extracted from a news segment can help to characterize and predict the presence
of propaganda techniques. Correlation analyses surfaced interesting patterns
that, for instance, the "loaded language" and "slogan" techniques are
negatively associated with valence and joy intensity but are positively
associated with anger, fear and sadness intensity. In contrast, "flag waving"
and "appeal to fear-prejudice" have the exact opposite pattern. Through
predictive experiments, results further indicate that whereas BERT-only
features obtained F1-score of 0.548, emotion intensity features and BERT hybrid
features were able to obtain F1-score of 0.570, when a simple feedforward
network was used as the classifier in both settings. On gold test data, our
system obtained micro-averaged F1-score of 0.558 on overall detection efficacy
over fourteen propaganda techniques. It performed relatively well in detecting
"loaded language" (F1 = 0.772), "name calling and labeling" (F1 = 0.673),
"doubt" (F1 = 0.604) and "flag waving" (F1 = 0.543).
| 2,020 | Computation and Language |
Temporal Mental Health Dynamics on Social Media | We describe a set of experiments for building a temporal mental health
dynamics system. We utilise a pre-existing methodology for distant-supervision
of mental health data mining from social media platforms and deploy the system
during the global COVID-19 pandemic as a case study. Despite the challenging
nature of the task, we produce encouraging results, both explicit to the global
pandemic and implicit to a global phenomenon, Christmas Depression, supported
by the literature. We propose a methodology for providing insight into temporal
mental health dynamics to be utilised for strategic decision-making.
| 2,020 | Computation and Language |
QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness
Using an Enhanced CT-BERT with Numeric Expressions | This paper describes the participation of the QMUL-SDS team for Task 1 of the
CLEF 2020 CheckThat! shared task. The purpose of this task is to determine the
check-worthiness of tweets about COVID-19 to identify and prioritise tweets
that need fact-checking. The overarching aim is to further support ongoing
efforts to protect the public from fake news and help people find reliable
information. We describe and analyse the results of our submissions. We show
that a CNN using COVID-Twitter-BERT (CT-BERT) enhanced with numeric expressions
can effectively boost performance from baseline results. We also show results
of training data augmentation with rumours on other topics. Our best system
ranked fourth in the task with encouraging outcomes showing potential for
improved results in the future.
| 2,020 | Computation and Language |
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