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Efficient Constituency Parsing by Pointing | We propose a novel constituency parsing model that casts the parsing problem
into a series of pointing tasks. Specifically, our model estimates the
likelihood of a span being a legitimate tree constituent via the pointing score
corresponding to the boundary words of the span. Our parsing model supports
efficient top-down decoding and our learning objective is able to enforce
structural consistency without resorting to the expensive CKY inference. The
experiments on the standard English Penn Treebank parsing task show that our
method achieves 92.78 F1 without using pre-trained models, which is higher than
all the existing methods with similar time complexity. Using pre-trained BERT,
our model achieves 95.48 F1, which is competitive with the state-of-the-art
while being faster. Our approach also establishes new state-of-the-art in
Basque and Swedish in the SPMRL shared tasks on multilingual constituency
parsing.
| 2,020 | Computation and Language |
Attention-Based Neural Networks for Sentiment Attitude Extraction using
Distant Supervision | In the sentiment attitude extraction task, the aim is to identify
<<attitudes>> -- sentiment relations between entities mentioned in text. In
this paper, we provide a study on attention-based context encoders in the
sentiment attitude extraction task. For this task, we adapt attentive context
encoders of two types: (1) feature-based; (2) self-based. In our study, we
utilize the corpus of Russian analytical texts RuSentRel and automatically
constructed news collection RuAttitudes for enriching the training set. We
consider the problem of attitude extraction as two-class (positive, negative)
and three-class (positive, negative, neutral) classification tasks for whole
documents. Our experiments with the RuSentRel corpus show that the three-class
classification models, which employ the RuAttitudes corpus for training, result
in 10% increase and extra 3% by F1, when model architectures include the
attention mechanism. We also provide the analysis of attention weight
distributions in dependence on the term type.
| 2,020 | Computation and Language |
Unsupervised Cross-lingual Representation Learning for Speech
Recognition | This paper presents XLSR which learns cross-lingual speech representations by
pretraining a single model from the raw waveform of speech in multiple
languages. We build on wav2vec 2.0 which is trained by solving a contrastive
task over masked latent speech representations and jointly learns a
quantization of the latents shared across languages. The resulting model is
fine-tuned on labeled data and experiments show that cross-lingual pretraining
significantly outperforms monolingual pretraining. On the CommonVoice
benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared
to the best known results. On BABEL, our approach improves word error rate by
16% relative compared to a comparable system. Our approach enables a single
multilingual speech recognition model which is competitive to strong individual
models. Analysis shows that the latent discrete speech representations are
shared across languages with increased sharing for related languages. We hope
to catalyze research in low-resource speech understanding by releasing XLSR-53,
a large model pretrained in 53 languages.
| 2,020 | Computation and Language |
Multilingual Jointly Trained Acoustic and Written Word Embeddings | Acoustic word embeddings (AWEs) are vector representations of spoken word
segments. AWEs can be learned jointly with embeddings of character sequences,
to generate phonetically meaningful embeddings of written words, or
acoustically grounded word embeddings (AGWEs). Such embeddings have been used
to improve speech retrieval, recognition, and spoken term discovery. In this
work, we extend this idea to multiple low-resource languages. We jointly train
an AWE model and an AGWE model, using phonetically transcribed data from
multiple languages. The pre-trained models can then be used for unseen
zero-resource languages, or fine-tuned on data from low-resource languages. We
also investigate distinctive features, as an alternative to phone labels, to
better share cross-lingual information. We test our models on word
discrimination tasks for twelve languages. When trained on eleven languages and
tested on the remaining unseen language, our model outperforms traditional
unsupervised approaches like dynamic time warping. After fine-tuning the
pre-trained models on one hour or even ten minutes of data from a new language,
performance is typically much better than training on only the target-language
data. We also find that phonetic supervision improves performance over
character sequences, and that distinctive feature supervision is helpful in
handling unseen phones in the target language.
| 2,020 | Computation and Language |
XREF: Entity Linking for Chinese News Comments with Supplementary
Article Reference | Automatic identification of mentioned entities in social media posts
facilitates quick digestion of trending topics and popular opinions.
Nonetheless, this remains a challenging task due to limited context and diverse
name variations. In this paper, we study the problem of entity linking for
Chinese news comments given mentions' spans. We hypothesize that comments often
refer to entities in the corresponding news article, as well as topics
involving the entities. We therefore propose a novel model, XREF, that
leverages attention mechanisms to (1) pinpoint relevant context within
comments, and (2) detect supporting entities from the news article. To improve
training, we make two contributions: (a) we propose a supervised attention loss
in addition to the standard cross entropy, and (b) we develop a weakly
supervised training scheme to utilize the large-scale unlabeled corpus. Two new
datasets in entertainment and product domains are collected and annotated for
experiments. Our proposed method outperforms previous methods on both datasets.
| 2,020 | Computation and Language |
Normalizing Text using Language Modelling based on Phonetics and String
Similarity | Social media networks and chatting platforms often use an informal version of
natural text. Adversarial spelling attacks also tend to alter the input text by
modifying the characters in the text. Normalizing these texts is an essential
step for various applications like language translation and text to speech
synthesis where the models are trained over clean regular English language. We
propose a new robust model to perform text normalization.
Our system uses the BERT language model to predict the masked words that
correspond to the unnormalized words. We propose two unique masking strategies
that try to replace the unnormalized words in the text with their root form
using a unique score based on phonetic and string similarity metrics.We use
human-centric evaluations where volunteers were asked to rank the normalized
text. Our strategies yield an accuracy of 86.7% and 83.2% which indicates the
effectiveness of our system in dealing with text normalization.
| 2,020 | Computation and Language |
Explainable CNN-attention Networks (C-Attention Network) for Automated
Detection of Alzheimer's Disease | In this work, we propose three explainable deep learning architectures to
automatically detect patients with Alzheimer`s disease based on their language
abilities. The architectures use: (1) only the part-of-speech features; (2)
only language embedding features and (3) both of these feature classes via a
unified architecture. We use self-attention mechanisms and interpretable
1-dimensional ConvolutionalNeural Network (CNN) to generate two types of
explanations of the model`s action: intra-class explanation and inter-class
explanation. The inter-class explanation captures the relative importance of
each of the different features in that class, while the inter-class explanation
captures the relative importance between the classes. Note that although we
have considered two classes of features in this paper, the architecture is
easily expandable to more classes because of its modularity. Extensive
experimentation and comparison with several recent models show that our method
outperforms these methods with an accuracy of 92.2% and F1 score of 0.952on the
DementiaBank dataset while being able to generate explanations. We show by
examples, how to generate these explanations using attention values.
| 2,021 | Computation and Language |
Neural Machine Translation for Multilingual Grapheme-to-Phoneme
Conversion | Grapheme-to-phoneme (G2P) models are a key component in Automatic Speech
Recognition (ASR) systems, such as the ASR system in Alexa, as they are used to
generate pronunciations for out-of-vocabulary words that do not exist in the
pronunciation lexicons (mappings like "e c h o" to "E k oU"). Most G2P systems
are monolingual and based on traditional joint-sequence based n-gram models
[1,2]. As an alternative, we present a single end-to-end trained neural G2P
model that shares same encoder and decoder across multiple languages. This
allows the model to utilize a combination of universal symbol inventories of
Latin-like alphabets and cross-linguistically shared feature representations.
Such model is especially useful in the scenarios of low resource languages and
code switching/foreign words, where the pronunciations in one language need to
be adapted to other locales or accents. We further experiment with word
language distribution vector as an additional training target in order to
improve system performance by helping the model decouple pronunciations across
a variety of languages in the parameter space. We show 7.2% average improvement
in phoneme error rate over low resource languages and no degradation over high
resource ones compared to monolingual baselines.
| 2,020 | Computation and Language |
A Simple Approach to Case-Based Reasoning in Knowledge Bases | We present a surprisingly simple yet accurate approach to reasoning in
knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of
case-based reasoning in classical artificial intelligence (AI). Consider the
task of finding a target entity given a source entity and a binary relation.
Our non-parametric approach derives crisp logical rules for each query by
finding multiple \textit{graph path patterns} that connect similar source
entities through the given relation. Using our method, we obtain new
state-of-the-art accuracy, outperforming all previous models, on NELL-995 and
FB-122. We also demonstrate that our model is robust in low data settings,
outperforming recently proposed meta-learning approaches
| 2,020 | Computation and Language |
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for
Predicting Financial Outcomes | In this paper, we automatically create sentiment dictionaries for predicting
financial outcomes. We compare three approaches: (I) manual adaptation of the
domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a
combination consisting of first manual, then automatic adaptation. In our
experiments, we demonstrate that the automatically adapted sentiment dictionary
outperforms the previous state of the art in predicting the financial outcomes
excess return and volatility. In particular, automatic adaptation performs
better than manual adaptation. In our analysis, we find that annotation based
on an expert's a priori belief about a word's meaning can be incorrect --
annotation should be performed based on the word's contexts in the target
domain instead.
| 2,020 | Computation and Language |
Neural Machine Translation For Paraphrase Generation | Training a spoken language understanding system, as the one in Alexa,
typically requires a large human-annotated corpus of data. Manual annotations
are expensive and time consuming. In Alexa Skill Kit (ASK) user experience with
the skill greatly depends on the amount of data provided by skill developer. In
this work, we present an automatic natural language generation system, capable
of generating both human-like interactions and annotations by the means of
paraphrasing. Our approach consists of machine translation (MT) inspired
encoder-decoder deep recurrent neural network. We evaluate our model on the
impact it has on ASK skill, intent, named entity classification accuracy and
sentence level coverage, all of which demonstrate significant improvements for
unseen skills on natural language understanding (NLU) models, trained on the
data augmented with paraphrases.
| 2,020 | Computation and Language |
Analyzing Effect of Repeated Reading on Oral Fluency and Narrative
Production for Computer-Assisted Language Learning | Repeated reading (RR) helps learners, who have little to no experience with
reading fluently to gain confidence, speed and process words automatically. The
benefits of repeated readings include helping all learners with fact recall,
aiding identification of learners' main ideas and vocabulary, increasing
comprehension, leading to faster reading as well as increasing word recognition
accuracy, and assisting struggling learners as they transition from
word-by-word reading to more meaningful phrasing. Thus, RR ultimately helps in
improvements of learners' oral fluency and narrative production. However, there
are no open audio datasets available on oral responses of learners based on
their RR practices. Therefore, in this paper, we present our dataset, discuss
its properties, and propose a method to assess oral fluency and narrative
production for learners of English using acoustic, prosodic, lexical and
syntactical characteristics. The results show that a CALL system can be
developed for assessing the improvements in learners' oral fluency and
narrative production.
| 2,020 | Computation and Language |
Learning Source Phrase Representations for Neural Machine Translation | The Transformer translation model (Vaswani et al., 2017) based on a
multi-head attention mechanism can be computed effectively in parallel and has
significantly pushed forward the performance of Neural Machine Translation
(NMT). Though intuitively the attentional network can connect distant words via
shorter network paths than RNNs, empirical analysis demonstrates that it still
has difficulty in fully capturing long-distance dependencies (Tang et al.,
2018). Considering that modeling phrases instead of words has significantly
improved the Statistical Machine Translation (SMT) approach through the use of
larger translation blocks ("phrases") and its reordering ability, modeling NMT
at phrase level is an intuitive proposal to help the model capture
long-distance relationships. In this paper, we first propose an attentive
phrase representation generation mechanism which is able to generate phrase
representations from corresponding token representations. In addition, we
incorporate the generated phrase representations into the Transformer
translation model to enhance its ability to capture long-distance
relationships. In our experiments, we obtain significant improvements on the
WMT 14 English-German and English-French tasks on top of the strong Transformer
baseline, which shows the effectiveness of our approach. Our approach helps
Transformer Base models perform at the level of Transformer Big models, and
even significantly better for long sentences, but with substantially fewer
parameters and training steps. The fact that phrase representations help even
in the big setting further supports our conjecture that they make a valuable
contribution to long-distance relations.
| 2,020 | Computation and Language |
IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment
Classification Using Candidate Sentence Generation and Selection | Code-mixing is the phenomenon of using multiple languages in the same
utterance of a text or speech. It is a frequently used pattern of communication
on various platforms such as social media sites, online gaming, product
reviews, etc. Sentiment analysis of the monolingual text is a well-studied
task. Code-mixing adds to the challenge of analyzing the sentiment of the text
due to the non-standard writing style. We present a candidate sentence
generation and selection based approach on top of the Bi-LSTM based neural
classifier to classify the Hinglish code-mixed text into one of the three
sentiment classes positive, negative, or neutral. The proposed approach shows
an improvement in the system performance as compared to the Bi-LSTM based
neural classifier. The results present an opportunity to understand various
other nuances of code-mixing in the textual data, such as humor-detection,
intent classification, etc.
| 2,020 | Computation and Language |
LPar -- A Distributed Multi Agent platform for building Polyglot, Omni
Channel and Industrial grade Natural Language Interfaces | The goal of serving and delighting customers in a personal and near human
like manner is very high on automation agendas of most Enterprises. Last few
years, have seen huge progress in Natural Language Processing domain which has
led to deployments of conversational agents in many enterprises. Most of the
current industrial deployments tend to use Monolithic Single Agent designs that
model the entire knowledge and skill of the Domain. While this approach is one
of the fastest to market, the monolithic design makes it very hard to scale
beyond a point. There are also challenges in seamlessly leveraging many tools
offered by sub fields of Natural Language Processing and Information Retrieval
in a single solution. The sub fields that can be leveraged to provide relevant
information are, Question and Answer system, Abstractive Summarization,
Semantic Search, Knowledge Graph etc. Current deployments also tend to be very
dependent on the underlying Conversational AI platform (open source or
commercial) , which is a challenge as this is a fast evolving space and no one
platform can be considered future proof even in medium term of 3-4 years.
Lately,there is also work done to build multi agent solutions that tend to
leverage a concept of master agent. While this has shown promise, this approach
still makes the master agent in itself difficult to scale. To address these
challenges, we introduce LPar, a distributed multi agent platform for large
scale industrial deployment of polyglot, diverse and inter-operable agents. The
asynchronous design of LPar supports dynamically expandable domain. We also
introduce multiple strategies available in the LPar system to elect the most
suitable agent to service a customer query.
| 2,020 | Computation and Language |
THEaiTRE: Artificial Intelligence to Write a Theatre Play | We present THEaiTRE, a starting project aimed at automatic generation of
theatre play scripts. This paper reviews related work and drafts an approach we
intend to follow. We plan to adopt generative neural language models and
hierarchical generation approaches, supported by summarization and machine
translation methods, and complemented with a human-in-the-loop approach.
| 2,020 | Computation and Language |
Graph Optimal Transport for Cross-Domain Alignment | Cross-domain alignment between two sets of entities (e.g., objects in an
image, words in a sentence) is fundamental to both computer vision and natural
language processing. Existing methods mainly focus on designing advanced
attention mechanisms to simulate soft alignment, with no training signals to
explicitly encourage alignment. The learned attention matrices are also dense
and lacks interpretability. We propose Graph Optimal Transport (GOT), a
principled framework that germinates from recent advances in Optimal Transport
(OT). In GOT, cross-domain alignment is formulated as a graph matching problem,
by representing entities into a dynamically-constructed graph. Two types of OT
distances are considered: (i) Wasserstein distance (WD) for node (entity)
matching; and (ii) Gromov-Wasserstein distance (GWD) for edge (structure)
matching. Both WD and GWD can be incorporated into existing neural network
models, effectively acting as a drop-in regularizer. The inferred transport
plan also yields sparse and self-normalized alignment, enhancing the
interpretability of the learned model. Experiments show consistent
outperformance of GOT over baselines across a wide range of tasks, including
image-text retrieval, visual question answering, image captioning, machine
translation, and text summarization.
| 2,020 | Computation and Language |
Dialog as a Vehicle for Lifelong Learning | Dialog systems research has primarily been focused around two main types of
applications - task-oriented dialog systems that learn to use clarification to
aid in understanding a goal, and open-ended dialog systems that are expected to
carry out unconstrained "chit chat" conversations. However, dialog interactions
can also be used to obtain various types of knowledge that can be used to
improve an underlying language understanding system, or other machine learning
systems that the dialog acts over. In this position paper, we present the
problem of designing dialog systems that enable lifelong learning as an
important challenge problem, in particular for applications involving
physically situated robots. We include examples of prior work in this
direction, and discuss challenges that remain to be addressed.
| 2,020 | Computation and Language |
Evaluation of Text Generation: A Survey | The paper surveys evaluation methods of natural language generation (NLG)
systems that have been developed in the last few years. We group NLG evaluation
methods into three categories: (1) human-centric evaluation metrics, (2)
automatic metrics that require no training, and (3) machine-learned metrics.
For each category, we discuss the progress that has been made and the
challenges still being faced, with a focus on the evaluation of recently
proposed NLG tasks and neural NLG models. We then present two examples for
task-specific NLG evaluations for automatic text summarization and long text
generation, and conclude the paper by proposing future research directions.
| 2,021 | Computation and Language |
LSBert: A Simple Framework for Lexical Simplification | Lexical simplification (LS) aims to replace complex words in a given sentence
with their simpler alternatives of equivalent meaning, to simplify the
sentence. Recently unsupervised lexical simplification approaches only rely on
the complex word itself regardless of the given sentence to generate candidate
substitutions, which will inevitably produce a large number of spurious
candidates. In this paper, we propose a lexical simplification framework LSBert
based on pretrained representation model Bert, that is capable of (1) making
use of the wider context when both detecting the words in need of
simplification and generating substitue candidates, and (2) taking five
high-quality features into account for ranking candidates, including Bert
prediction order, Bert-based language model, and the paraphrase database PPDB,
in addition to the word frequency and word similarity commonly used in other LS
methods. We show that our system outputs lexical simplifications that are
grammatically correct and semantically appropriate, and obtains obvious
improvement compared with these baselines, outperforming the state-of-the-art
by 29.8 Accuracy points on three well-known benchmarks.
| 2,020 | Computation and Language |
What they do when in doubt: a study of inductive biases in seq2seq
learners | Sequence-to-sequence (seq2seq) learners are widely used, but we still have
only limited knowledge about what inductive biases shape the way they
generalize. We address that by investigating how popular seq2seq learners
generalize in tasks that have high ambiguity in the training data. We use SCAN
and three new tasks to study learners' preferences for memorization,
arithmetic, hierarchical, and compositional reasoning. Further, we connect to
Solomonoff's theory of induction and propose to use description length as a
principled and sensitive measure of inductive biases.
In our experimental study, we find that LSTM-based learners can learn to
perform counting, addition, and multiplication by a constant from a single
training example. Furthermore, Transformer and LSTM-based learners show a bias
toward the hierarchical induction over the linear one, while CNN-based learners
prefer the opposite. On the SCAN dataset, we find that CNN-based, and, to a
lesser degree, Transformer- and LSTM-based learners have a preference for
compositional generalization over memorization. Finally, across all our
experiments, description length proved to be a sensitive measure of inductive
biases.
| 2,021 | Computation and Language |
ProVe -- Self-supervised pipeline for automated product replacement and
cold-starting based on neural language models | In retail vertical industries, businesses are dealing with human limitation
of quickly understanding and adapting to new purchasing behaviors. Moreover,
retail businesses need to overcome the human limitation of properly managing a
massive selection of products/brands/categories. These limitations lead to
deficiencies from both commercial (e.g. loss of sales, decrease in customer
satisfaction) and operational perspective (e.g. out-of-stock, over-stock). In
this paper, we propose a pipeline approach based on Natural Language
Understanding, for recommending the most suitable replacements for products
that are out-of-stock. Moreover, we will propose a solution for managing
products that were newly introduced in a retailer's portfolio with almost no
transactional history. This solution will help businesses: automatically assign
the new products to the right category; recommend complementary products for
cross-sell from day 1; perform sales predictions even with almost no
transactional history. Finally, the vector space model resulted by applying the
pipeline presented in this paper is directly used as semantic information in
deep learning-based demand forecasting solutions, leading to more accurate
predictions. The whole research and experimentation process have been done
using real-life private transactional data, however the source code is
available on https://github.com/Lummetry/ProVe
| 2,021 | Computation and Language |
Pre-training via Paraphrasing | We introduce MARGE, a pre-trained sequence-to-sequence model learned with an
unsupervised multi-lingual multi-document paraphrasing objective. MARGE
provides an alternative to the dominant masked language modeling paradigm,
where we self-supervise the reconstruction of target text by retrieving a set
of related texts (in many languages) and conditioning on them to maximize the
likelihood of generating the original. We show it is possible to jointly learn
to do retrieval and reconstruction, given only a random initialization. The
objective noisily captures aspects of paraphrase, translation, multi-document
summarization, and information retrieval, allowing for strong zero-shot
performance on several tasks. For example, with no additional task-specific
training we achieve BLEU scores of up to 35.8 for document translation. We
further show that fine-tuning gives strong performance on a range of
discriminative and generative tasks in many languages, making MARGE the most
generally applicable pre-training method to date.
| 2,020 | Computation and Language |
BERTology Meets Biology: Interpreting Attention in Protein Language
Models | Transformer architectures have proven to learn useful representations for
protein classification and generation tasks. However, these representations
present challenges in interpretability. In this work, we demonstrate a set of
methods for analyzing protein Transformer models through the lens of attention.
We show that attention: (1) captures the folding structure of proteins,
connecting amino acids that are far apart in the underlying sequence, but
spatially close in the three-dimensional structure, (2) targets binding sites,
a key functional component of proteins, and (3) focuses on progressively more
complex biophysical properties with increasing layer depth. We find this
behavior to be consistent across three Transformer architectures (BERT, ALBERT,
XLNet) and two distinct protein datasets. We also present a three-dimensional
visualization of the interaction between attention and protein structure. Code
for visualization and analysis is available at
https://github.com/salesforce/provis.
| 2,021 | Computation and Language |
Uncertainty-aware Self-training for Text Classification with Few Labels | Recent success of large-scale pre-trained language models crucially hinge on
fine-tuning them on large amounts of labeled data for the downstream task, that
are typically expensive to acquire. In this work, we study self-training as one
of the earliest semi-supervised learning approaches to reduce the annotation
bottleneck by making use of large-scale unlabeled data for the target task.
Standard self-training mechanism randomly samples instances from the unlabeled
pool to pseudo-label and augment labeled data. In this work, we propose an
approach to improve self-training by incorporating uncertainty estimates of the
underlying neural network leveraging recent advances in Bayesian deep learning.
Specifically, we propose (i) acquisition functions to select instances from the
unlabeled pool leveraging Monte Carlo (MC) Dropout, and (ii) learning mechanism
leveraging model confidence for self-training. As an application, we focus on
text classification on five benchmark datasets. We show our methods leveraging
only 20-30 labeled samples per class for each task for training and for
validation can perform within 3% of fully supervised pre-trained language
models fine-tuned on thousands of labeled instances with an aggregate accuracy
of 91% and improving by upto 12% over baselines.
| 2,020 | Computation and Language |
Video-Grounded Dialogues with Pretrained Generation Language Models | Pre-trained language models have shown remarkable success in improving
various downstream NLP tasks due to their ability to capture dependencies in
textual data and generate natural responses. In this paper, we leverage the
power of pre-trained language models for improving video-grounded dialogue,
which is very challenging and involves complex features of different dynamics:
(1) Video features which can extend across both spatial and temporal
dimensions; and (2) Dialogue features which involve semantic dependencies over
multiple dialogue turns. We propose a framework by extending GPT-2 models to
tackle these challenges by formulating video-grounded dialogue tasks as a
sequence-to-sequence task, combining both visual and textual representation
into a structured sequence, and fine-tuning a large pre-trained GPT-2 network.
Our framework allows fine-tuning language models to capture dependencies across
multiple modalities over different levels of information: spatio-temporal level
in video and token-sentence level in dialogue context. We achieve promising
improvement on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark from
DSTC7, which supports a potential direction in this line of research.
| 2,020 | Computation and Language |
String-based methods for tonal harmony: A corpus study of Haydn's string
quartets | This chapter considers how string-based methods might be adapted to address
music-analytic questions related to the discovery of musical organization, with
particular attention devoted to the analysis of tonal harmony. I begin by
applying the taxonomy of mental organization proposed by Mandler (1979) to the
concept of musical organization. Using this taxonomy as a guide, I then present
evidence for three principles of tonal harmony -- recurrence, syntax, and
recursion -- using a corpus of Haydn string quartets.
| 2,020 | Computation and Language |
Mind The Facts: Knowledge-Boosted Coherent Abstractive Text
Summarization | Neural models have become successful at producing abstractive summaries that
are human-readable and fluent. However, these models have two critical
shortcomings: they often don't respect the facts that are either included in
the source article or are known to humans as commonsense knowledge, and they
don't produce coherent summaries when the source article is long. In this work,
we propose a novel architecture that extends Transformer encoder-decoder
architecture in order to improve on these shortcomings. First, we incorporate
entity-level knowledge from the Wikidata knowledge graph into the
encoder-decoder architecture. Injecting structural world knowledge from
Wikidata helps our abstractive summarization model to be more fact-aware.
Second, we utilize the ideas used in Transformer-XL language model in our
proposed encoder-decoder architecture. This helps our model with producing
coherent summaries even when the source article is long. We test our model on
CNN/Daily Mail summarization dataset and show improvements on ROUGE scores over
the baseline Transformer model. We also include model predictions for which our
model accurately conveys the facts, while the baseline Transformer model
doesn't.
| 2,020 | Computation and Language |
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with
Bilingual Semantic Similarity Rewards | Cross-lingual text summarization aims at generating a document summary in one
language given input in another language. It is a practically important but
under-explored task, primarily due to the dearth of available data. Existing
methods resort to machine translation to synthesize training data, but such
pipeline approaches suffer from error propagation. In this work, we propose an
end-to-end cross-lingual text summarization model. The model uses reinforcement
learning to directly optimize a bilingual semantic similarity metric between
the summaries generated in a target language and gold summaries in a source
language. We also introduce techniques to pre-train the model leveraging
monolingual summarization and machine translation objectives. Experimental
results in both English--Chinese and English--German cross-lingual
summarization settings demonstrate the effectiveness of our methods. In
addition, we find that reinforcement learning models with bilingual semantic
similarity as rewards generate more fluent sentences than strong baselines.
| 2,020 | Computation and Language |
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant
Supervision | We study the open-domain named entity recognition (NER) problem under distant
supervision. The distant supervision, though does not require large amounts of
manual annotations, yields highly incomplete and noisy distant labels via
external knowledge bases. To address this challenge, we propose a new
computational framework -- BOND, which leverages the power of pre-trained
language models (e.g., BERT and RoBERTa) to improve the prediction performance
of NER models. Specifically, we propose a two-stage training algorithm: In the
first stage, we adapt the pre-trained language model to the NER tasks using the
distant labels, which can significantly improve the recall and precision; In
the second stage, we drop the distant labels, and propose a self-training
approach to further improve the model performance. Thorough experiments on 5
benchmark datasets demonstrate the superiority of BOND over existing distantly
supervised NER methods. The code and distantly labeled data have been released
in https://github.com/cliang1453/BOND.
| 2,020 | Computation and Language |
Self-Attention Networks for Intent Detection | Self-attention networks (SAN) have shown promising performance in various
Natural Language Processing (NLP) scenarios, especially in machine translation.
One of the main points of SANs is the strength of capturing long-range and
multi-scale dependencies from the data. In this paper, we present a novel
intent detection system which is based on a self-attention network and a
Bi-LSTM. Our approach shows improvement by using a transformer model and deep
averaging network-based universal sentence encoder compared to previous
solutions. We evaluate the system on Snips, Smart Speaker, Smart Lights, and
ATIS datasets by different evaluation metrics. The performance of the proposed
model is compared with LSTM with the same datasets.
| 2,020 | Computation and Language |
Rethinking Positional Encoding in Language Pre-training | In this work, we investigate the positional encoding methods used in language
pre-training (e.g., BERT) and identify several problems in the existing
formulations. First, we show that in the absolute positional encoding, the
addition operation applied on positional embeddings and word embeddings brings
mixed correlations between the two heterogeneous information resources. It may
bring unnecessary randomness in the attention and further limit the
expressiveness of the model. Second, we question whether treating the position
of the symbol \texttt{[CLS]} the same as other words is a reasonable design,
considering its special role (the representation of the entire sentence) in the
downstream tasks. Motivated from above analysis, we propose a new positional
encoding method called \textbf{T}ransformer with \textbf{U}ntied
\textbf{P}ositional \textbf{E}ncoding (TUPE). In the self-attention module,
TUPE computes the word contextual correlation and positional correlation
separately with different parameterizations and then adds them together. This
design removes the mixed and noisy correlations over heterogeneous embeddings
and offers more expressiveness by using different projection matrices.
Furthermore, TUPE unties the \texttt{[CLS]} symbol from other positions, making
it easier to capture information from all positions. Extensive experiments and
ablation studies on GLUE benchmark demonstrate the effectiveness of the
proposed method. Codes and models are released at
https://github.com/guolinke/TUPE.
| 2,021 | Computation and Language |
Progressive Generation of Long Text with Pretrained Language Models | Large-scale language models (LMs) pretrained on massive corpora of text, such
as GPT-2, are powerful open-domain text generators. However, as our systematic
examination reveals, it is still challenging for such models to generate
coherent long passages of text (e.g., 1000 tokens), especially when the models
are fine-tuned to the target domain on a small corpus. Previous
planning-then-generation methods also fall short of producing such long text in
various domains. To overcome the limitations, we propose a simple but effective
method of generating text in a progressive manner, inspired by generating
images from low to high resolution. Our method first produces domain-specific
content keywords and then progressively refines them into complete passages in
multiple stages. The simple design allows our approach to take advantage of
pretrained LMs at each stage and effectively adapt to any target domain given
only a small set of examples. We conduct a comprehensive empirical study with a
broad set of evaluation metrics, and show that our approach significantly
improves upon the fine-tuned large LMs and various planning-then-generation
methods in terms of quality and sample efficiency. Human evaluation also
validates that our model generations are more coherent.
| 2,021 | Computation and Language |
Mapping Topic Evolution Across Poetic Traditions | Poetic traditions across languages evolved differently, but we find that
certain semantic topics occur in several of them, albeit sometimes with
temporal delay, or with diverging trajectories over time. We apply Latent
Dirichlet Allocation (LDA) to poetry corpora of four languages, i.e. German
(52k poems), English (85k poems), Russian (18k poems), and Czech (80k poems).
We align and interpret salient topics, their trend over time (1600--1925 A.D.),
showing similarities and disparities across poetic traditions with a few select
topics, and use their trajectories over time to pinpoint specific literary
epochs.
| 2,020 | Computation and Language |
Combine Convolution with Recurrent Networks for Text Classification | Convolutional neural network (CNN) and recurrent neural network (RNN) are two
popular architectures used in text classification. Traditional methods to
combine the strengths of the two networks rely on streamlining them or
concatenating features extracted from them. In this paper, we propose a novel
method to keep the strengths of the two networks to a great extent. In the
proposed model, a convolutional neural network is applied to learn a 2D weight
matrix where each row reflects the importance of each word from different
aspects. Meanwhile, we use a bi-directional RNN to process each word and employ
a neural tensor layer that fuses forward and backward hidden states to get word
representations. In the end, the weight matrix and word representations are
combined to obtain the representation in a 2D matrix form for the text. We
carry out experiments on a number of datasets for text classification. The
experimental results confirm the effectiveness of the proposed method.
| 2,020 | Computation and Language |
Answering Questions on COVID-19 in Real-Time | The recent outbreak of the novel coronavirus is wreaking havoc on the world
and researchers are struggling to effectively combat it. One reason why the
fight is difficult is due to the lack of information and knowledge. In this
work, we outline our effort to contribute to shrinking this knowledge vacuum by
creating covidAsk, a question answering (QA) system that combines biomedical
text mining and QA techniques to provide answers to questions in real-time. Our
system also leverages information retrieval (IR) approaches to provide
entity-level answers that are complementary to QA models. Evaluation of
covidAsk is carried out by using a manually created dataset called COVID-19
Questions which is based on information from various sources, including the CDC
and the WHO. We hope our system will be able to aid researchers in their search
for knowledge and information not only for COVID-19, but for future pandemics
as well.
| 2,020 | Computation and Language |
A Framework for Pre-processing of Social Media Feeds based on Integrated
Local Knowledge Base | Most of the previous studies on the semantic analysis of social media feeds
have not considered the issue of ambiguity that is associated with slangs,
abbreviations, and acronyms that are embedded in social media posts. These
noisy terms have implicit meanings and form part of the rich semantic context
that must be analysed to gain complete insights from social media feeds. This
paper proposes an improved framework for pre-processing of social media feeds
for better performance. To do this, the use of an integrated knowledge base
(ikb) which comprises a local knowledge source (Naijalingo), urban dictionary
and internet slang was combined with the adapted Lesk algorithm to facilitate
semantic analysis of social media feeds. Experimental results showed that the
proposed approach performed better than existing methods when it was tested on
three machine learning models, which are support vector machines, multilayer
perceptron, and convolutional neural networks. The framework had an accuracy of
94.07% on a standardized dataset, and 99.78% on localised dataset when used to
extract sentiments from tweets. The improved performance on the localised
dataset reveals the advantage of integrating the use of local knowledge sources
into the process of analysing social media feeds particularly in interpreting
slangs/acronyms/abbreviations that have contextually rooted meanings.
| 2,020 | Computation and Language |
Is Japanese gendered language used on Twitter ? A large scale study | This study analyzes the usage of Japanese gendered language on Twitter.
Starting from a collection of 408 million Japanese tweets from 2015 till 2019
and an additional sample of 2355 manually classified Twitter accounts timelines
into gender and categories (politicians, musicians, etc). A large scale textual
analysis is performed on this corpus to identify and examine sentence-final
particles (SFPs) and first-person pronouns appearing in the texts. It turns out
that gendered language is in fact used also on Twitter, in about 6% of the
tweets, and that the prescriptive classification into "male" and "female"
language does not always meet the expectations, with remarkable exceptions.
Further, SFPs and pronouns show increasing or decreasing trends, indicating an
evolution of the language used on Twitter.
| 2,022 | Computation and Language |
Hinting Semantic Parsing with Statistical Word Sense Disambiguation | The task of Semantic Parsing can be approximated as a transformation of an
utterance into a logical form graph where edges represent semantic roles and
nodes represent word senses. The resulting representation should be capture the
meaning of the utterance and be suitable for reasoning. Word senses and
semantic roles are interdependent, meaning errors in assigning word senses can
cause errors in assigning semantic roles and vice versa. While statistical
approaches to word sense disambiguation outperform logical, rule-based semantic
parsers for raw word sense assignment, these statistical word sense
disambiguation systems do not produce the rich role structure or detailed
semantic representation of the input. In this work, we provide hints from a
statistical WSD system to guide a logical semantic parser to produce better
semantic type assignments while maintaining the soundness of the resulting
logical forms. We observe an improvement of up to 10.5% in F-score, however we
find that this improvement comes at a cost to the structural integrity of the
parse
| 2,020 | Computation and Language |
A Transformer-based joint-encoding for Emotion Recognition and Sentiment
Analysis | Understanding expressed sentiment and emotions are two crucial factors in
human multimodal language. This paper describes a Transformer-based
joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment
Analysis. In addition to use the Transformer architecture, our approach relies
on a modular co-attention and a glimpse layer to jointly encode one or more
modalities. The proposed solution has also been submitted to the ACL20: Second
Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI
dataset. The code to replicate the presented experiments is open-source:
https://github.com/jbdel/MOSEI_UMONS.
| 2,020 | Computation and Language |
Improving Sequence Tagging for Vietnamese Text Using Transformer-based
Neural Models | This paper describes our study on using mutilingual BERT embeddings and some
new neural models for improving sequence tagging tasks for the Vietnamese
language. We propose new model architectures and evaluate them extensively on
two named entity recognition datasets of VLSP 2016 and VLSP 2018, and on two
part-of-speech tagging datasets of VLSP 2010 and VLSP 2013. Our proposed models
outperform existing methods and achieve new state-of-the-art results. In
particular, we have pushed the accuracy of part-of-speech tagging to 95.40% on
the VLSP 2010 corpus, to 96.77% on the VLSP 2013 corpus; and the F1 score of
named entity recognition to 94.07% on the VLSP 2016 corpus, to 90.31% on the
VLSP 2018 corpus. Our code and pre-trained models viBERT and vELECTRA are
released as open source to facilitate adoption and further research.
| 2,020 | Computation and Language |
Measuring Memorization Effect in Word-Level Neural Networks Probing | Multiple studies have probed representations emerging in neural networks
trained for end-to-end NLP tasks and examined what word-level linguistic
information may be encoded in the representations. In classical probing, a
classifier is trained on the representations to extract the target linguistic
information. However, there is a threat of the classifier simply memorizing the
linguistic labels for individual words, instead of extracting the linguistic
abstractions from the representations, thus reporting false positive results.
While considerable efforts have been made to minimize the memorization problem,
the task of actually measuring the amount of memorization happening in the
classifier has been understudied so far. In our work, we propose a simple
general method for measuring the memorization effect, based on a symmetric
selection of comparable sets of test words seen versus unseen in training. Our
method can be used to explicitly quantify the amount of memorization happening
in a probing setup, so that an adequate setup can be chosen and the results of
the probing can be interpreted with a reliability estimate. We exemplify this
by showcasing our method on a case study of probing for part of speech in a
trained neural machine translation encoder.
| 2,020 | Computation and Language |
Want to Identify, Extract and Normalize Adverse Drug Reactions in
Tweets? Use RoBERTa | This paper presents our approach for task 2 and task 3 of Social Media Mining
for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate
adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary
classification. Task3 involves extracting ADR mentions and then mapping them to
MedDRA codes. Extracting ADR mentions is treated as sequence labeling and
normalizing ADR mentions is treated as multi-class classification. Our system
is based on pre-trained language model RoBERTa and it achieves a) F1-score of
58% in task2 which is 12% more than the average score b) relaxed F1-score of
70.1% in ADR extraction of task 3 which is 13.7% more than the average score
and relaxed F1-score of 35% in ADR extraction + normalization of task3 which is
5.8% more than the average score. Overall, our models achieve promising results
in both the tasks with significant improvements over average scores.
| 2,020 | Computation and Language |
Leveraging Subword Embeddings for Multinational Address Parsing | Address parsing consists of identifying the segments that make up an address
such as a street name or a postal code. Because of its importance for tasks
like record linkage, address parsing has been approached with many techniques.
Neural network methods defined a new state-of-the-art for address parsing.
While this approach yielded notable results, previous work has only focused on
applying neural networks to achieve address parsing of addresses from one
source country. We propose an approach in which we employ subword embeddings
and a Recurrent Neural Network architecture to build a single model capable of
learning to parse addresses from multiple countries at the same time while
taking into account the difference in languages and address formatting systems.
We achieved accuracies around 99 % on the countries used for training with no
pre-processing nor post-processing needed. We explore the possibility of
transferring the address parsing knowledge obtained by training on some
countries' addresses to others with no further training in a zero-shot transfer
learning setting. We achieve good results for 80 % of the countries (33 out of
41), almost 50 % of which (20 out of 41) is near state-of-the-art performance.
In addition, we propose an open-source Python implementation of our trained
models.
| 2,020 | Computation and Language |
Multichannel CNN with Attention for Text Classification | Recent years, the approaches based on neural networks have shown remarkable
potential for sentence modeling. There are two main neural network structures:
recurrent neural network (RNN) and convolution neural network (CNN). RNN can
capture long term dependencies and store the semantics of the previous
information in a fixed-sized vector. However, RNN is a biased model and its
ability to extract global semantics is restricted by the fixed-sized vector.
Alternatively, CNN is able to capture n-gram features of texts by utilizing
convolutional filters. But the width of convolutional filters restricts its
performance. In order to combine the strengths of the two kinds of networks and
alleviate their shortcomings, this paper proposes Attention-based Multichannel
Convolutional Neural Network (AMCNN) for text classification. AMCNN utilizes a
bi-directional long short-term memory to encode the history and future
information of words into high dimensional representations, so that the
information of both the front and back of the sentence can be fully expressed.
Then the scalar attention and vectorial attention are applied to obtain
multichannel representations. The scalar attention can calculate the word-level
importance and the vectorial attention can calculate the feature-level
importance. In the classification task, AMCNN uses a CNN structure to cpture
word relations on the representations generated by the scalar and vectorial
attention mechanism instead of calculating the weighted sums. It can
effectively extract the n-gram features of the text. The experimental results
on the benchmark datasets demonstrate that AMCNN achieves better performance
than state-of-the-art methods. In addition, the visualization results verify
the semantic richness of multichannel representations.
| 2,020 | Computation and Language |
Natural Backdoor Attack on Text Data | Recently, advanced NLP models have seen a surge in the usage of various
applications. This raises the security threats of the released models. In
addition to the clean models' unintentional weaknesses, {\em i.e.,} adversarial
attacks, the poisoned models with malicious intentions are much more dangerous
in real life. However, most existing works currently focus on the adversarial
attacks on NLP models instead of positioning attacks, also named
\textit{backdoor attacks}. In this paper, we first propose the \textit{natural
backdoor attacks} on NLP models. Moreover, we exploit the various attack
strategies to generate trigger on text data and investigate different types of
triggers based on modification scope, human recognition, and special cases.
Last, we evaluate the backdoor attacks, and the results show the excellent
performance of with 100\% backdoor attacks success rate and sacrificing of
0.83\% on the text classification task.
| 2,021 | Computation and Language |
Towards the Study of Morphological Processing of the Tangkhul Language | There is no or little work on natural language processing of Tangkhul
language. The current work is a humble beginning of morphological processing of
this language using an unsupervised approach. We use a small corpus collected
from different sources of text books, short stories and articles of other
topics. Based on the experiments carried out, the morpheme identification task
using morphessor gives reasonable and interesting output despite using a small
corpus.
| 2,017 | Computation and Language |
Universal linguistic inductive biases via meta-learning | How do learners acquire languages from the limited data available to them?
This process must involve some inductive biases - factors that affect how a
learner generalizes - but it is unclear which inductive biases can explain
observed patterns in language acquisition. To facilitate computational modeling
aimed at addressing this question, we introduce a framework for giving
particular linguistic inductive biases to a neural network model; such a model
can then be used to empirically explore the effects of those inductive biases.
This framework disentangles universal inductive biases, which are encoded in
the initial values of a neural network's parameters, from non-universal
factors, which the neural network must learn from data in a given language. The
initial state that encodes the inductive biases is found with meta-learning, a
technique through which a model discovers how to acquire new languages more
easily via exposure to many possible languages. By controlling the properties
of the languages that are used during meta-learning, we can control the
inductive biases that meta-learning imparts. We demonstrate this framework with
a case study based on syllable structure. First, we specify the inductive
biases that we intend to give our model, and then we translate those inductive
biases into a space of languages from which a model can meta-learn. Finally,
using existing analysis techniques, we verify that our approach has imparted
the linguistic inductive biases that it was intended to impart.
| 2,020 | Computation and Language |
Learning Sparse Prototypes for Text Generation | Prototype-driven text generation uses non-parametric models that first choose
from a library of sentence "prototypes" and then modify the prototype to
generate the output text. While effective, these methods are inefficient at
test time as a result of needing to store and index the entire training corpus.
Further, existing methods often require heuristics to identify which prototypes
to reference at training time. In this paper, we propose a novel generative
model that automatically learns a sparse prototype support set that,
nonetheless, achieves strong language modeling performance. This is achieved by
(1) imposing a sparsity-inducing prior on the prototype selection distribution,
and (2) utilizing amortized variational inference to learn a prototype
retrieval function. In experiments, our model outperforms previous
prototype-driven language models while achieving up to a 1000x memory
reduction, as well as a 1000x speed-up at test time. More interestingly, we
show that the learned prototypes are able to capture semantics and syntax at
different granularity as we vary the sparsity of prototype selection, and that
certain sentence attributes can be controlled by specifying the prototype for
generation.
| 2,020 | Computation and Language |
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense
reasoNing (UNION) | In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing
(UNION) system submitted for Task C of the SemEval2020 Task 4, which is to
generate a reason explaining why a given false statement is non-sensical.
However, we found in the early experiments that simple adaptations such as
fine-tuning GPT2 often yield dull and non-informative generations (e.g. simple
negations). In order to generate more meaningful explanations, we propose
UNION, a unified end-to-end framework, to utilize several existing commonsense
datasets so that it allows a model to learn more dynamics under the scope of
commonsense reasoning. In order to perform model selection efficiently,
accurately and promptly, we also propose a couple of auxiliary automatic
evaluation metrics so that we can extensively compare the models from different
perspectives. Our submitted system not only results in a good performance in
the proposed metrics but also outperforms its competitors with the highest
achieved score of 2.10 for human evaluation while remaining a BLEU score of
15.7. Our code is made publicly available at GitHub.
| 2,020 | Computation and Language |
A Data-driven Neural Network Architecture for Sentiment Analysis | The fabulous results of convolution neural networks in image-related tasks,
attracted attention of text mining, sentiment analysis and other text analysis
researchers. It is however difficult to find enough data for feeding such
networks, optimize their parameters, and make the right design choices when
constructing network architectures. In this paper we present the creation steps
of two big datasets of song emotions. We also explore usage of convolution and
max-pooling neural layers on song lyrics, product and movie review text
datasets. Three variants of a simple and flexible neural network architecture
are also compared. Our intention was to spot any important patterns that can
serve as guidelines for parameter optimization of similar models. We also
wanted to identify architecture design choices which lead to high performing
sentiment analysis models. To this end, we conducted a series of experiments
with neural architectures of various configurations. Our results indicate that
parallel convolutions of filter lengths up to three are usually enough for
capturing relevant text features. Also, max-pooling region size should be
adapted to the length of text documents for producing the best feature maps.
Top results we got are obtained with feature maps of lengths 6 to 18. An
improvement on future neural network models for sentiment analysis, could be
generating sentiment polarity prediction of documents using aggregation of
predictions on smaller excerpt of the entire text.
| 2,019 | Computation and Language |
GShard: Scaling Giant Models with Conditional Computation and Automatic
Sharding | Neural network scaling has been critical for improving the model quality in
many real-world machine learning applications with vast amounts of training
data and compute. Although this trend of scaling is affirmed to be a sure-fire
approach for better model quality, there are challenges on the path such as the
computation cost, ease of programming, and efficient implementation on parallel
devices. GShard is a module composed of a set of lightweight annotation APIs
and an extension to the XLA compiler. It provides an elegant way to express a
wide range of parallel computation patterns with minimal changes to the
existing model code. GShard enabled us to scale up multilingual neural machine
translation Transformer model with Sparsely-Gated Mixture-of-Experts beyond 600
billion parameters using automatic sharding. We demonstrate that such a giant
model can efficiently be trained on 2048 TPU v3 accelerators in 4 days to
achieve far superior quality for translation from 100 languages to English
compared to the prior art.
| 2,020 | Computation and Language |
Correction of Faulty Background Knowledge based on Condition Aware and
Revise Transformer for Question Answering | The study of question answering has received increasing attention in recent
years. This work focuses on providing an answer that compatible with both user
intent and conditioning information corresponding to the question, such as
delivery status and stock information in e-commerce. However, these conditions
may be wrong or incomplete in real-world applications. Although existing
question answering systems have considered the external information, such as
categorical attributes and triples in knowledge base, they all assume that the
external information is correct and complete. To alleviate the effect of
defective condition values, this paper proposes condition aware and revise
Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value
based on the whole conversation and original conditions values, and (2) it
encodes the revised conditions and utilizes the conditions embedding to select
an answer. Experimental results on a real-world customer service dataset
demonstrate that the CAR-Transformer can still select an appropriate reply when
conditions corresponding to the question exist wrong or missing values, and
substantially outperforms baseline models on automatic and human evaluations.
The proposed CAR-Transformer can be extended to other NLP tasks which need to
consider conditioning information.
| 2,020 | Computation and Language |
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning | To build a high-quality open-domain chatbot, we introduce the effective
training process of PLATO-2 via curriculum learning. There are two stages
involved in the learning process. In the first stage, a coarse-grained
generation model is trained to learn response generation under the simplified
framework of one-to-one mapping. In the second stage, a fine-grained generative
model augmented with latent variables and an evaluation model are further
trained to generate diverse responses and to select the best response,
respectively. PLATO-2 was trained on both Chinese and English data, whose
effectiveness and superiority are verified through comprehensive evaluations,
achieving new state-of-the-art results.
| 2,021 | Computation and Language |
Technical Report: Auxiliary Tuning and its Application to Conditional
Text Generation | We introduce a simple and efficient method, called Auxiliary Tuning, for
adapting a pre-trained Language Model to a novel task; we demonstrate this
approach on the task of conditional text generation. Our approach supplements
the original pre-trained model with an auxiliary model that shifts the output
distribution according to the target task. The auxiliary model is trained by
adding its logits to the pre-trained model logits and maximizing the likelihood
of the target task output. Our method imposes no constraints on the auxiliary
architecture. In particular, the auxiliary model can ingest additional input
relevant to the target task, independently from the pre-trained model's input.
Furthermore, mixing the models at the logits level provides a natural
probabilistic interpretation of the method. Our method achieved similar results
to training from scratch for several different tasks, while using significantly
fewer resources for training; we share a specific example of text generation
conditioned on keywords.
| 2,020 | Computation and Language |
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in
Word Embeddings | Language representations are known to carry stereotypical biases and, as a
result, lead to biased predictions in downstream tasks. While existing methods
are effective at mitigating biases by linear projection, such methods are too
aggressive: they not only remove bias, but also erase valuable information from
word embeddings. We develop new measures for evaluating specific information
retention that demonstrate the tradeoff between bias removal and information
retention. To address this challenge, we propose OSCaR (Orthogonal Subspace
Correction and Rectification), a bias-mitigating method that focuses on
disentangling biased associations between concepts instead of removing concepts
wholesale. Our experiments on gender biases show that OSCaR is a well-balanced
approach that ensures that semantic information is retained in the embeddings
and bias is also effectively mitigated.
| 2,021 | Computation and Language |
Adversarial Mutual Information for Text Generation | Recent advances in maximizing mutual information (MI) between the source and
target have demonstrated its effectiveness in text generation. However,
previous works paid little attention to modeling the backward network of MI
(i.e., dependency from the target to the source), which is crucial to the
tightness of the variational information maximization lower bound. In this
paper, we propose Adversarial Mutual Information (AMI): a text generation
framework which is formed as a novel saddle point (min-max) optimization aiming
to identify joint interactions between the source and target. Within this
framework, the forward and backward networks are able to iteratively promote or
demote each other's generated instances by comparing the real and synthetic
data distributions. We also develop a latent noise sampling strategy that
leverages random variations at the high-level semantic space to enhance the
long term dependency in the generation process. Extensive experiments based on
different text generation tasks demonstrate that the proposed AMI framework can
significantly outperform several strong baselines, and we also show that AMI
has potential to lead to a tighter lower bound of maximum mutual information
for the variational information maximization problem.
| 2,020 | Computation and Language |
Transferability of Natural Language Inference to Biomedical Question
Answering | Biomedical question answering (QA) is a challenging task due to the scarcity
of data and the requirement of domain expertise. Pre-trained language models
have been used to address these issues. Recently, learning relationships
between sentence pairs has been proved to improve performance in general QA. In
this paper, we focus on applying BioBERT to transfer the knowledge of natural
language inference (NLI) to biomedical QA. We observe that BioBERT trained on
the NLI dataset obtains better performance on Yes/No (+5.59%), Factoid
(+0.53%), List type (+13.58%) questions compared to performance obtained in a
previous challenge (BioASQ 7B Phase B). We present a sequential transfer
learning method that significantly performed well in the 8th BioASQ Challenge
(Phase B). In sequential transfer learning, the order in which tasks are
fine-tuned is important. We measure an unanswerable rate of the extractive QA
setting when the formats of factoid and list type questions are converted to
the format of the Stanford Question Answering Dataset (SQuAD).
| 2,021 | Computation and Language |
Multimodal Text Style Transfer for Outdoor Vision-and-Language
Navigation | One of the most challenging topics in Natural Language Processing (NLP) is
visually-grounded language understanding and reasoning. Outdoor
vision-and-language navigation (VLN) is such a task where an agent follows
natural language instructions and navigates a real-life urban environment. Due
to the lack of human-annotated instructions that illustrate intricate urban
scenes, outdoor VLN remains a challenging task to solve. This paper introduces
a Multimodal Text Style Transfer (MTST) learning approach and leverages
external multimodal resources to mitigate data scarcity in outdoor navigation
tasks. We first enrich the navigation data by transferring the style of the
instructions generated by Google Maps API, then pre-train the navigator with
the augmented external outdoor navigation dataset. Experimental results show
that our MTST learning approach is model-agnostic, and our MTST approach
significantly outperforms the baseline models on the outdoor VLN task,
improving task completion rate by 8.7% relatively on the test set.
| 2,021 | Computation and Language |
SemEval-2020 Task 4: Commonsense Validation and Explanation | In this paper, we present SemEval-2020 Task 4, Commonsense Validation and
Explanation (ComVE), which includes three subtasks, aiming to evaluate whether
a system can distinguish a natural language statement that makes sense to
humans from one that does not, and provide the reasons. Specifically, in our
first subtask, the participating systems are required to choose from two
natural language statements of similar wording the one that makes sense and the
one does not. The second subtask additionally asks a system to select the key
reason from three options why a given statement does not make sense. In the
third subtask, a participating system needs to generate the reason. We finally
attracted 39 teams participating at least one of the three subtasks. For
Subtask A and Subtask B, the performances of top-ranked systems are close to
that of humans. However, for Subtask C, there is still a relatively large gap
between systems and human performance. The dataset used in our task can be
found at https://github.com/wangcunxiang/SemEval2020-
Task4-Commonsense-Validation-and-Explanation; The leaderboard can be found at
https://competitions.codalab.org/competitions/21080#results.
| 2,020 | Computation and Language |
So What's the Plan? Mining Strategic Planning Documents | In this paper we present a corpus of Russian strategic planning documents,
RuREBus. This project is grounded both from language technology and
e-government perspectives. Not only new language sources and tools are being
developed, but also their applications to e-goverment research. We demonstrate
the pipeline for creating a text corpus from scratch. First, the annotation
schema is designed. Next texts are marked up using human-in-the-loop strategy,
so that preliminary annotations are derived from a machine learning model and
are manually corrected. The amount of annotated texts is large enough to
showcase what insights can be gained from RuREBus.
| 2,020 | Computation and Language |
Latent Compositional Representations Improve Systematic Generalization
in Grounded Question Answering | Answering questions that involve multi-step reasoning requires decomposing
them and using the answers of intermediate steps to reach the final answer.
However, state-of-the-art models in grounded question answering often do not
explicitly perform decomposition, leading to difficulties in generalization to
out-of-distribution examples. In this work, we propose a model that computes a
representation and denotation for all question spans in a bottom-up,
compositional manner using a CKY-style parser. Our model induces latent trees,
driven by end-to-end (the answer) supervision only. We show that this inductive
bias towards tree structures dramatically improves systematic generalization to
out-of-distribution examples, compared to strong baselines on an arithmetic
expressions benchmark as well as on CLOSURE, a dataset that focuses on
systematic generalization for grounded question answering. On this challenging
dataset, our model reaches an accuracy of 96.1%, significantly higher than
prior models that almost perfectly solve the task on a random, in-distribution
split.
| 2,020 | Computation and Language |
Iterative Paraphrastic Augmentation with Discriminative Span Alignment | We introduce a novel paraphrastic augmentation strategy based on
sentence-level lexically constrained paraphrasing and discriminative span
alignment. Our approach allows for the large-scale expansion of existing
resources, or the rapid creation of new resources from a small,
manually-produced seed corpus. We illustrate our framework on the Berkeley
FrameNet Project, a large-scale language understanding effort spanning more
than two decades of human labor. Based on roughly four days of collecting
training data for the alignment model and approximately one day of parallel
compute, we automatically generate 495,300 unique (Frame, Trigger) combinations
annotated in context, a roughly 50x expansion atop FrameNet v1.7.
| 2,020 | Computation and Language |
Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower
Neural Network | Recent advancements in medical entity linking have been applied in the area
of scientific literature and social media data. However, with the adoption of
telemedicine and conversational agents such as Alexa in healthcare settings,
medical name inference has become an important task. Medication name inference
is the task of mapping user friendly medication names from a free-form text to
a concept in a normalized medication list. This is challenging due to the
differences in the use of medical terminology from health care professionals
and user conversations coming from the lay public. We begin with mapping
descriptive medication phrases (DMP) to standard medication names (SMN). Given
the prescriptions of each patient, we want to provide them with the flexibility
of referring to the medication in their preferred ways. We approach this as a
ranking problem which maps SMN to DMP by ordering the list of medications in
the patient's prescription list obtained from pharmacies. Furthermore, we
leveraged the output of intermediate layers and performed medication
clustering. We present the Medication Inference Model (MIM) achieving
state-of-the-art results. By incorporating medical entities based attention, we
have obtained further improvement for ranking models.
| 2,020 | Computation and Language |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing
Report Generation | To combat COVID-19, both clinicians and scientists need to digest vast
amounts of relevant biomedical knowledge in scientific literature to understand
the disease mechanism and related biological functions. We have developed a
novel and comprehensive knowledge discovery framework, COVID-KG to extract
fine-grained multimedia knowledge elements (entities and their visual chemical
structures, relations, and events) from scientific literature. We then exploit
the constructed multimedia knowledge graphs (KGs) for question answering and
report generation, using drug repurposing as a case study. Our framework also
provides detailed contextual sentences, subfigures, and knowledge subgraphs as
evidence.
| 2,021 | Computation and Language |
Knowledge-Aware Language Model Pretraining | How much knowledge do pretrained language models hold? Recent research
observed that pretrained transformers are adept at modeling semantics but it is
unclear to what degree they grasp human knowledge, or how to ensure they do so.
In this paper we incorporate knowledge-awareness in language model pretraining
without changing the transformer architecture, inserting explicit knowledge
layers, or adding external storage of semantic information. Rather, we simply
signal the existence of entities to the input of the transformer in
pretraining, with an entity-extended tokenizer; and at the output, with an
additional entity prediction task. Our experiments show that solely by adding
these entity signals in pretraining, significantly more knowledge is packed
into the transformer parameters: we observe improved language modeling
accuracy, factual correctness in LAMA knowledge probing tasks, and semantics in
the hidden representations through edge probing.We also show that our
knowledge-aware language model (KALM) can serve as a drop-in replacement for
GPT-2 models, significantly improving downstream tasks like zero-shot
question-answering with no task-related training.
| 2,021 | Computation and Language |
Relevance-guided Supervision for OpenQA with ColBERT | Systems for Open-Domain Question Answering (OpenQA) generally depend on a
retriever for finding candidate passages in a large corpus and a reader for
extracting answers from those passages. In much recent work, the retriever is a
learned component that uses coarse-grained vector representations of questions
and passages. We argue that this modeling choice is insufficiently expressive
for dealing with the complexity of natural language questions. To address this,
we define ColBERT-QA, which adapts the scalable neural retrieval model ColBERT
to OpenQA. ColBERT creates fine-grained interactions between questions and
passages. We propose an efficient weak supervision strategy that iteratively
uses ColBERT to create its own training data. This greatly improves OpenQA
retrieval on Natural Questions, SQuAD, and TriviaQA, and the resulting system
attains state-of-the-art extractive OpenQA performance on all three datasets.
| 2,021 | Computation and Language |
Lightme: Analysing Language in Internet Support Groups for Mental Health | Background: Assisting moderators to triage harmful posts in Internet Support
Groups is relevant to ensure its safe use. Automated text classification
methods analysing the language expressed in posts of online forums is a
promising solution. Methods: Natural Language Processing and Machine Learning
technologies were used to build a triage post classifier using a dataset from
Reachout mental health forum for young people. Results: When comparing with the
state-of-the-art, a solution mainly based on features from lexical resources,
received the best classification performance for the crisis posts (52%), which
is the most severe class. Six salient linguistic characteristics were found
when analysing the crisis post; 1) posts expressing hopelessness, 2) short
posts expressing concise negative emotional responses, 3) long posts expressing
variations of emotions, 4) posts expressing dissatisfaction with available
health services, 5) posts utilising storytelling, and 6) posts expressing users
seeking advice from peers during a crisis. Conclusion: It is possible to build
a competitive triage classifier using features derived only from the textual
content of the post. Further research needs to be done in order to translate
our quantitative and qualitative findings into features, as it may improve
overall performance.
| 2,020 | Computation and Language |
Facts as Experts: Adaptable and Interpretable Neural Memory over
Symbolic Knowledge | Massive language models are the core of modern NLP modeling and have been
shown to encode impressive amounts of commonsense and factual information.
However, that knowledge exists only within the latent parameters of the model,
inaccessible to inspection and interpretation, and even worse, factual
information memorized from the training corpora is likely to become stale as
the world changes. Knowledge stored as parameters will also inevitably exhibit
all of the biases inherent in the source materials. To address these problems,
we develop a neural language model that includes an explicit interface between
symbolically interpretable factual information and subsymbolic neural
knowledge. We show that this model dramatically improves performance on two
knowledge-intensive question-answering tasks. More interestingly, the model can
be updated without re-training by manipulating its symbolic representations. In
particular this model allows us to add new facts and overwrite existing ones in
ways that are not possible for earlier models.
| 2,020 | Computation and Language |
Can We Achieve More with Less? Exploring Data Augmentation for Toxic
Comment Classification | This paper tackles one of the greatest limitations in Machine Learning: Data
Scarcity. Specifically, we explore whether high accuracy classifiers can be
built from small datasets, utilizing a combination of data augmentation
techniques and machine learning algorithms. In this paper, we experiment with
Easy Data Augmentation (EDA) and Backtranslation, as well as with three popular
learning algorithms, Logistic Regression, Support Vector Machine (SVM), and
Bidirectional Long Short-Term Memory Network (Bi-LSTM). For our
experimentation, we utilize the Wikipedia Toxic Comments dataset so that in the
process of exploring the benefits of data augmentation, we can develop a model
to detect and classify toxic speech in comments to help fight back against
cyberbullying and online harassment. Ultimately, we found that data
augmentation techniques can be used to significantly boost the performance of
classifiers and are an excellent strategy to combat lack of data in NLP
problems.
| 2,020 | Computation and Language |
Fact-based Text Editing | We propose a novel text editing task, referred to as \textit{fact-based text
editing}, in which the goal is to revise a given document to better describe
the facts in a knowledge base (e.g., several triples). The task is important in
practice because reflecting the truth is a common requirement in text editing.
First, we propose a method for automatically generating a dataset for research
on fact-based text editing, where each instance consists of a draft text, a
revised text, and several facts represented in triples. We apply the method
into two public table-to-text datasets, obtaining two new datasets consisting
of 233k and 37k instances, respectively. Next, we propose a new neural network
architecture for fact-based text editing, called \textsc{FactEditor}, which
edits a draft text by referring to given facts using a buffer, a stream, and a
memory. A straightforward approach to address the problem would be to employ an
encoder-decoder model. Our experimental results on the two datasets show that
\textsc{FactEditor} outperforms the encoder-decoder approach in terms of
fidelity and fluency. The results also show that \textsc{FactEditor} conducts
inference faster than the encoder-decoder approach.
| 2,021 | Computation and Language |
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template
Reconstruction Strategy for ComVE | This paper introduces our systems for the first two subtasks of SemEval
Task4: Commonsense Validation and Explanation. To clarify the intention for
judgment and inject contrastive information for selection, we propose the input
reconstruction strategy with prompt templates. Specifically, we formalize the
subtasks into the multiple-choice question answering format and construct the
input with the prompt templates, then, the final prediction of question
answering is considered as the result of subtasks. Experimental results show
that our approaches achieve significant performance compared with the baseline
systems. Our approaches secure the third rank on both official test sets of the
first two subtasks with an accuracy of 96.4 and an accuracy of 94.3
respectively.
| 2,020 | Computation and Language |
Project PIAF: Building a Native French Question-Answering Dataset | Motivated by the lack of data for non-English languages, in particular for
the evaluation of downstream tasks such as Question Answering, we present a
participatory effort to collect a native French Question Answering Dataset.
Furthermore, we describe and publicly release the annotation tool developed for
our collection effort, along with the data obtained and preliminary baselines.
| 2,020 | Computation and Language |
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy
Channel for Robust Pharmacological Entity Detection | Named entity recognition has been extensively studied on English news texts.
However, the transfer to other domains and languages is still a challenging
problem. In this paper, we describe the system with which we participated in
the first subtrack of the PharmaCoNER competition of the BioNLP Open Shared
Tasks 2019. Aiming at pharmacological entity detection in Spanish texts, the
task provides a non-standard domain and language setting. However, we propose
an architecture that requires neither language nor domain expertise. We treat
the task as a sequence labeling task and experiment with attention-based
embedding selection and the training on automatically annotated data to further
improve our system's performance. Our system achieves promising results,
especially by combining the different techniques, and reaches up to 88.6% F1 in
the competition.
| 2,020 | Computation and Language |
NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical
Document De-Identification | Natural language processing has huge potential in the medical domain which
recently led to a lot of research in this field. However, a prerequisite of
secure processing of medical documents, e.g., patient notes and clinical
trials, is the proper de-identification of privacy-sensitive information. In
this paper, we describe our NLNDE system, with which we participated in the
MEDDOCAN competition, the medical document anonymization task of IberLEF 2019.
We address the task of detecting and classifying protected health information
from Spanish data as a sequence-labeling problem and investigate different
embedding methods for our neural network. Despite dealing in a non-standard
language and domain setting, the NLNDE system achieves promising results in the
competition.
| 2,020 | Computation and Language |
Bidirectional Encoder Representations from Transformers (BERT): A
sentiment analysis odyssey | The purpose of the study is to investigate the relative effectiveness of four
different sentiment analysis techniques: (1) unsupervised lexicon-based model
using Sent WordNet; (2) traditional supervised machine learning model using
logistic regression; (3) supervised deep learning model using Long Short-Term
Memory (LSTM); and, (4) advanced supervised deep learning models using
Bidirectional Encoder Representations from Transformers (BERT). We use publicly
available labeled corpora of 50,000 movie reviews originally posted on internet
movie database (IMDB) for analysis using Sent WordNet lexicon, logistic
regression, LSTM, and BERT. The first three models were run on CPU based system
whereas BERT was run on GPU based system. The sentiment classification
performance was evaluated based on accuracy, precision, recall, and F1 score.
The study puts forth two key insights: (1) relative efficacy of four highly
advanced and widely used sentiment analysis techniques; (2) undisputed
superiority of pre-trained advanced supervised deep learning BERT model in
sentiment analysis from text data. This study provides professionals in
analytics industry and academicians working on text analysis key insight
regarding comparative classification performance evaluation of key sentiment
analysis techniques, including the recently developed BERT. This is the first
research endeavor to compare the advanced pre-trained supervised deep learning
model of BERT vis-\`a-vis other sentiment analysis models of LSTM, logistic
regression, and Sent WordNet.
| 2,020 | Computation and Language |
Processing South Asian Languages Written in the Latin Script: the
Dakshina Dataset | This paper describes the Dakshina dataset, a new resource consisting of text
in both the Latin and native scripts for 12 South Asian languages. The dataset
includes, for each language: 1) native script Wikipedia text; 2) a romanization
lexicon; and 3) full sentence parallel data in both a native script of the
language and the basic Latin alphabet. We document the methods used for
preparation and selection of the Wikipedia text in each language; collection of
attested romanizations for sampled lexicons; and manual romanization of
held-out sentences from the native script collections. We additionally provide
baseline results on several tasks made possible by the dataset, including
single word transliteration, full sentence transliteration, and language
modeling of native script and romanized text. Keywords: romanization,
transliteration, South Asian languages
| 2,020 | Computation and Language |
Sequential Domain Adaptation through Elastic Weight Consolidation for
Sentiment Analysis | Elastic Weight Consolidation (EWC) is a technique used in overcoming
catastrophic forgetting between successive tasks trained on a neural network.
We use this phenomenon of information sharing between tasks for domain
adaptation. Training data for tasks such as sentiment analysis (SA) may not be
fairly represented across multiple domains. Domain Adaptation (DA) aims to
build algorithms that leverage information from source domains to facilitate
performance on an unseen target domain. We propose a model-independent
framework - Sequential Domain Adaptation (SDA). SDA draws on EWC for training
on successive source domains to move towards a general domain solution, thereby
solving the problem of domain adaptation. We test SDA on convolutional,
recurrent, and attention-based architectures. Our experiments show that the
proposed framework enables simple architectures such as CNNs to outperform
complex state-of-the-art models in domain adaptation of SA. In addition, we
observe that the effectiveness of a harder first Anti-Curriculum ordering of
source domains leads to maximum performance.
| 2,020 | Computation and Language |
Leveraging Passage Retrieval with Generative Models for Open Domain
Question Answering | Generative models for open domain question answering have proven to be
competitive, without resorting to external knowledge. While promising, this
approach requires to use models with billions of parameters, which are
expensive to train and query. In this paper, we investigate how much these
models can benefit from retrieving text passages, potentially containing
evidence. We obtain state-of-the-art results on the Natural Questions and
TriviaQA open benchmarks. Interestingly, we observe that the performance of
this method significantly improves when increasing the number of retrieved
passages. This is evidence that generative models are good at aggregating and
combining evidence from multiple passages.
| 2,021 | Computation and Language |
Bayesian multilingual topic model for zero-shot cross-lingual topic
identification | This paper presents a Bayesian multilingual topic model for learning
language-independent document embeddings. Our model learns to represent the
documents in the form of Gaussian distributions, thereby encoding the
uncertainty in its covariance. We propagate the learned uncertainties through
linear classifiers for zero-shot cross-lingual topic identification. Our
experiments on 5 language Europarl and Reuters (MLDoc) corpora show that the
proposed model outperforms multi-lingual word embedding and BiLSTM sentence
encoder based systems with significant margins in the majority of the transfer
directions. Moreover, our system trained under a single day on a single GPU
with much lower amounts of data performs competitively as compared to the
state-of-the-art universal BiLSTM sentence encoder trained on 93 languages. Our
experimental analysis shows that the amount of parallel data improves the
overall performance of embeddings. Nonetheless, exploiting the uncertainties is
always beneficial.
| 2,020 | Computation and Language |
Detecting Ongoing Events Using Contextual Word and Sentence Embeddings | This paper introduces the Ongoing Event Detection (OED) task, which is a
specific Event Detection task where the goal is to detect ongoing event
mentions only, as opposed to historical, future, hypothetical, or other forms
or events that are neither fresh nor current. Any application that needs to
extract structured information about ongoing events from unstructured texts can
take advantage of an OED system. The main contribution of this paper are the
following: (1) it introduces the OED task along with a dataset manually labeled
for the task; (2) it presents the design and implementation of an RNN model for
the task that uses BERT embeddings to define contextual word and contextual
sentence embeddings as attributes, which to the best of our knowledge were
never used before for detecting ongoing events in news; (3) it presents an
extensive empirical evaluation that includes (i) the exploration of different
architectures and hyperparameters, (ii) an ablation test to study the impact of
each attribute, and (iii) a comparison with a replication of a state-of-the-art
model. The results offer several insights into the importance of contextual
embeddings and indicate that the proposed approach is effective in the OED
task, outperforming the baseline models.
| 2,021 | Computation and Language |
On-The-Fly Information Retrieval Augmentation for Language Models | Here we experiment with the use of information retrieval as an augmentation
for pre-trained language models. The text corpus used in information retrieval
can be viewed as form of episodic memory which grows over time. By augmenting
GPT 2.0 with information retrieval we achieve a zero shot 15% relative
reduction in perplexity on Gigaword corpus without any re-training. We also
validate our IR augmentation on an event co-reference task.
| 2,020 | Computation and Language |
Generating Informative Dialogue Responses with Keywords-Guided Networks | Recently, open-domain dialogue systems have attracted growing attention. Most
of them use the sequence-to-sequence (Seq2Seq) architecture to generate
responses. However, traditional Seq2Seq-based open-domain dialogue models tend
to generate generic and safe responses, which are less informative, unlike
human responses. In this paper, we propose a simple but effective
keywords-guided Sequence-to-Sequence model (KW-Seq2Seq) which uses keywords
information as guidance to generate open-domain dialogue responses.
Specifically, KW-Seq2Seq first uses a keywords decoder to predict some topic
keywords, and then generates the final response under the guidance of them.
Extensive experiments demonstrate that the KW-Seq2Seq model produces more
informative, coherent and fluent responses, yielding substantive gain in both
automatic and human evaluation metrics.
| 2,020 | Computation and Language |
Playing with Words at the National Library of Sweden -- Making a Swedish
BERT | This paper introduces the Swedish BERT ("KB-BERT") developed by the KBLab for
data-driven research at the National Library of Sweden (KB). Building on recent
efforts to create transformer-based BERT models for languages other than
English, we explain how we used KB's collections to create and train a new
language-specific BERT model for Swedish. We also present the results of our
model in comparison with existing models - chiefly that produced by the Swedish
Public Employment Service, Arbetsf\"ormedlingen, and Google's multilingual
M-BERT - where we demonstrate that KB-BERT outperforms these in a range of NLP
tasks from named entity recognition (NER) to part-of-speech tagging (POS). Our
discussion highlights the difficulties that continue to exist given the lack of
training data and testbeds for smaller languages like Swedish. We release our
model for further exploration and research here:
https://github.com/Kungbib/swedish-bert-models .
| 2,020 | Computation and Language |
Reading Comprehension in Czech via Machine Translation and Cross-lingual
Transfer | Reading comprehension is a well studied task, with huge training datasets in
English. This work focuses on building reading comprehension systems for Czech,
without requiring any manually annotated Czech training data. First of all, we
automatically translated SQuAD 1.1 and SQuAD 2.0 datasets to Czech to create
training and development data, which we release at
http://hdl.handle.net/11234/1-3249. We then trained and evaluated several BERT
and XLM-RoBERTa baseline models. However, our main focus lies in cross-lingual
transfer models. We report that a XLM-RoBERTa model trained on English data and
evaluated on Czech achieves very competitive performance, only approximately 2
percent points worse than a~model trained on the translated Czech data. This
result is extremely good, considering the fact that the model has not seen any
Czech data during training. The cross-lingual transfer approach is very
flexible and provides a reading comprehension in any language, for which we
have enough monolingual raw texts.
| 2,020 | Computation and Language |
TICO-19: the Translation Initiative for Covid-19 | The COVID-19 pandemic is the worst pandemic to strike the world in over a
century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating
to vulnerable populations the means by which they can protect themselves. To
this end, the collaborators forming the Translation Initiative for COvid-19
(TICO-19) have made test and development data available to AI and MT
researchers in 35 different languages in order to foster the development of
tools and resources for improving access to information about COVID-19 in these
languages. In addition to 9 high-resourced, "pivot" languages, the team is
targeting 26 lesser resourced languages, in particular languages of Africa,
South Asia and South-East Asia, whose populations may be the most vulnerable to
the spread of the virus. The same data is translated into all of the languages
represented, meaning that testing or development can be done for any pairing of
languages in the set. Further, the team is converting the test and development
data into translation memories (TMXs) that can be used by localizers from and
to any of the languages.
| 2,020 | Computation and Language |
Exploration and Discovery of the COVID-19 Literature through Semantic
Visualization | We are developing semantic visualization techniques in order to enhance
exploration and enable discovery over large datasets of complex networks of
relations. Semantic visualization is a method of enabling exploration and
discovery over large datasets of complex networks by exploiting the semantics
of the relations in them. This involves (i) NLP to extract named entities,
relations and knowledge graphs from the original data; (ii) indexing the output
and creating representations for all relevant entities and relations that can
be visualized in many different ways, e.g., as tag clouds, heat maps, graphs,
etc.; (iii) applying parameter reduction operations to the extracted relations,
creating "relation containers", or functional entities that can also be
visualized using the same methods, allowing the visualization of multiple
relations, partial pathways, and exploration across multiple dimensions. Our
hope is that this will enable the discovery of novel inferences over relations
in complex data that otherwise would go unnoticed. We have applied this to
analysis of the recently released CORD-19 dataset.
| 2,020 | Computation and Language |
Language-agnostic BERT Sentence Embedding | While BERT is an effective method for learning monolingual sentence
embeddings for semantic similarity and embedding based transfer learning
(Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have
yet to be explored. We systematically investigate methods for learning
multilingual sentence embeddings by combining the best methods for learning
monolingual and cross-lingual representations including: masked language
modeling (MLM), translation language modeling (TLM) (Conneau and Lample, 2019),
dual encoder translation ranking (Guo et al., 2018), and additive margin
softmax (Yang et al., 2019a). We show that introducing a pre-trained
multilingual language model dramatically reduces the amount of parallel
training data required to achieve good performance by 80%. Composing the best
of these methods produces a model that achieves 83.7% bi-text retrieval
accuracy over 112 languages on Tatoeba, well above the 65.5% achieved by
Artetxe and Schwenk (2019b), while still performing competitively on
monolingual transfer learning benchmarks (Conneau and Kiela, 2018). Parallel
data mined from CommonCrawl using our best model is shown to train competitive
NMT models for en-zh and en-de. We publicly release our best multilingual
sentence embedding model for 109+ languages at https://tfhub.dev/google/LaBSE.
| 2,022 | Computation and Language |
Abstractive and mixed summarization for long-single documents | The lack of diversity in the datasets available for automatic summarization
of documents has meant that the vast majority of neural models for automatic
summarization have been trained with news articles. These datasets are
relatively small, with an average size of about 600 words, and the models
trained with such data sets see their performance limited to short documents.
In order to surmount this problem, this paper uses scientific papers as the
dataset on which different models are trained. These models have been chosen
based on their performance on the CNN/Daily Mail data set, so that the highest
ranked model of each architectural variant is selected. In this work, six
different models are compared, two with an RNN architecture, one with a CNN
architecture, two with a Transformer architecture and one with a Transformer
architecture combined with reinforcement learning. The results from this work
show that those models that use a hierarchical encoder to model the structure
of the document has a better performance than the rest.
| 2,020 | Computation and Language |
El Departamento de Nosotros: How Machine Translated Corpora Affects
Language Models in MRC Tasks | Pre-training large-scale language models (LMs) requires huge amounts of text
corpora. LMs for English enjoy ever growing corpora of diverse language
resources. However, less resourced languages and their mono- and multilingual
LMs often struggle to obtain bigger datasets. A typical approach in this case
implies using machine translation of English corpora to a target language. In
this work, we study the caveats of applying directly translated corpora for
fine-tuning LMs for downstream natural language processing tasks and
demonstrate that careful curation along with post-processing lead to improved
performance and overall LMs robustness. In the empirical evaluation, we perform
a comparison of directly translated against curated Spanish SQuAD datasets on
both user and system levels. Further experimental results on XQuAD and MLQA
transfer-learning evaluation question answering tasks show that presumably
multilingual LMs exhibit more resilience to machine translation artifacts in
terms of the exact match score.
| 2,020 | Computation and Language |
Robust Prediction of Punctuation and Truecasing for Medical ASR | Automatic speech recognition (ASR) systems in the medical domain that focus
on transcribing clinical dictations and doctor-patient conversations often pose
many challenges due to the complexity of the domain. ASR output typically
undergoes automatic punctuation to enable users to speak naturally, without
having to vocalise awkward and explicit punctuation commands, such as "period",
"add comma" or "exclamation point", while truecasing enhances user readability
and improves the performance of downstream NLP tasks. This paper proposes a
conditional joint modeling framework for prediction of punctuation and
truecasing using pretrained masked language models such as BERT, BioBERT and
RoBERTa. We also present techniques for domain and task specific adaptation by
fine-tuning masked language models with medical domain data. Finally, we
improve the robustness of the model against common errors made in ASR by
performing data augmentation. Experiments performed on dictation and
conversational style corpora show that our proposed model achieves ~5% absolute
improvement on ground truth text and ~10% improvement on ASR outputs over
baseline models under F1 metric.
| 2,020 | Computation and Language |
Text Data Augmentation: Towards better detection of spear-phishing
emails | Text data augmentation, i.e., the creation of new textual data from an
existing text, is challenging. Indeed, augmentation transformations should take
into account language complexity while being relevant to the target Natural
Language Processing (NLP) task (e.g., Machine Translation, Text
Classification). Initially motivated by an application of Business Email
Compromise (BEC) detection, we propose a corpus and task agnostic augmentation
framework used as a service to augment English texts within our company. Our
proposal combines different methods, utilizing BERT language model, multi-step
back-translation and heuristics. We show that our augmentation framework
improves performances on several text classification tasks using publicly
available models and corpora as well as on a BEC detection task. We also
provide a comprehensive argumentation about the limitations of our augmentation
framework.
| 2,021 | Computation and Language |
Low Rank Fusion based Transformers for Multimodal Sequences | Our senses individually work in a coordinated fashion to express our
emotional intentions. In this work, we experiment with modeling
modality-specific sensory signals to attend to our latent multimodal emotional
intentions and vice versa expressed via low-rank multimodal fusion and
multimodal transformers. The low-rank factorization of multimodal fusion
amongst the modalities helps represent approximate multiplicative latent signal
interactions. Motivated by the work of~\cite{tsai2019MULT} and~\cite{Liu_2018},
we present our transformer-based cross-fusion architecture without any
over-parameterization of the model. The low-rank fusion helps represent the
latent signal interactions while the modality-specific attention helps focus on
relevant parts of the signal. We present two methods for the Multimodal
Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP
datasets and show that our models have lesser parameters, train faster and
perform comparably to many larger fusion-based architectures.
| 2,020 | Computation and Language |
Pynsett: A programmable relation extractor | This paper proposes a programmable relation extraction method for the English
language by parsing texts into semantic graphs. A person can define rules in
plain English that act as matching patterns onto the graph representation.
These rules are designed to capture the semantic content of the documents,
allowing for flexibility and ad-hoc entities. Relation extraction is a complex
task that typically requires sizable training corpora. The method proposed here
is ideal for extracting specialized ontologies in a limited collection of
documents.
| 2,020 | Computation and Language |
Sentiment Analysis on Social Media Content | Nowadays, people from all around the world use social media sites to share
information. Twitter for example is a platform in which users send, read posts
known as tweets and interact with different communities. Users share their
daily lives, post their opinions on everything such as brands and places.
Companies can benefit from this massive platform by collecting data related to
opinions on them. The aim of this paper is to present a model that can perform
sentiment analysis of real data collected from Twitter. Data in Twitter is
highly unstructured which makes it difficult to analyze. However, our proposed
model is different from prior work in this field because it combined the use of
supervised and unsupervised machine learning algorithms. The process of
performing sentiment analysis as follows: Tweet extracted directly from Twitter
API, then cleaning and discovery of data performed. After that the data were
fed into several models for the purpose of training. Each tweet extracted
classified based on its sentiment whether it is a positive, negative or
neutral. Data were collected on two subjects McDonalds and KFC to show which
restaurant has more popularity. Different machine learning algorithms were
used. The result from these models were tested using various testing metrics
like cross validation and f-score. Moreover, our model demonstrates strong
performance on mining texts extracted directly from Twitter.
| 2,020 | Computation and Language |
Birds of a Feather Flock Together: Satirical News Detection via Language
Model Differentiation | Satirical news is regularly shared in modern social media because it is
entertaining with smartly embedded humor. However, it can be harmful to society
because it can sometimes be mistaken as factual news, due to its deceptive
character. We found that in satirical news, the lexical and pragmatical
attributes of the context are the key factors in amusing the readers. In this
work, we propose a method that differentiates the satirical news and true news.
It takes advantage of satirical writing evidence by leveraging the difference
between the prediction loss of two language models, one trained on true news
and the other on satirical news, when given a new news article. We compute
several statistical metrics of language model prediction loss as features,
which are then used to conduct downstream classification. The proposed method
is computationally effective because the language models capture the language
usage differences between satirical news documents and traditional news
documents, and are sensitive when applied to documents outside their domains.
| 2,020 | Computation and Language |
Sentiment Analysis on Customer Responses | Sentiment analysis is one of the fastest spreading research areas in computer
science, making it challenging to keep track of all the activities in the area.
We present a customer feedback reviews on product, where we utilize opinion
mining, text mining and sentiments, which has affected the surrounded world by
changing their opinion on a specific product. Data used in this study are
online product reviews collected from Amazon.com. We performed a comparative
sentiment analysis of retrieved reviews. This research paper provides you with
sentimental analysis of various smart phone opinions on smart phones dividing
them Positive, Negative and Neutral Behaviour.
| 2,020 | Computation and Language |
News Sentiment Analysis | Modern technological era has reshaped traditional lifestyle in several
domains. The medium of publishing news and events has become faster with the
advancement of Information Technology. IT has also been flooded with immense
amounts of data, which is being published every minute of every day, by
millions of users, in the shape of comments, blogs, news sharing through blogs,
social media micro-blogging websites and many more. Manual traversal of such
huge data is a challenging job, thus, sophisticated methods are acquired to
perform this task automatically and efficiently. News reports events that
comprise of emotions - good, bad, neutral. Sentiment analysis is utilized to
investigate human emotions present in textual information. This paper presents
a lexicon-based approach for sentiment analysis of news articles. The
experiments have been performed on BBC news data set, which expresses the
applicability and validation of the adopted approach.
| 2,020 | Computation and Language |
Unsupervised Paraphrasing via Deep Reinforcement Learning | Paraphrasing is expressing the meaning of an input sentence in different
wording while maintaining fluency (i.e., grammatical and syntactical
correctness). Most existing work on paraphrasing use supervised models that are
limited to specific domains (e.g., image captions). Such models can neither be
straightforwardly transferred to other domains nor generalize well, and
creating labeled training data for new domains is expensive and laborious. The
need for paraphrasing across different domains and the scarcity of labeled
training data in many such domains call for exploring unsupervised paraphrase
generation methods. We propose Progressive Unsupervised Paraphrasing (PUP): a
novel unsupervised paraphrase generation method based on deep reinforcement
learning (DRL). PUP uses a variational autoencoder (trained using a
non-parallel corpus) to generate a seed paraphrase that warm-starts the DRL
model. Then, PUP progressively tunes the seed paraphrase guided by our novel
reward function which combines semantic adequacy, language fluency, and
expression diversity measures to quantify the quality of the generated
paraphrases in each iteration without needing parallel sentences. Our extensive
experimental evaluation shows that PUP outperforms unsupervised
state-of-the-art paraphrasing techniques in terms of both automatic metrics and
user studies on four real datasets. We also show that PUP outperforms
domain-adapted supervised algorithms on several datasets. Our evaluation also
shows that PUP achieves a great trade-off between semantic similarity and
diversity of expression.
| 2,020 | Computation and Language |
EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based
Ensemble Methods | This paper provides a method to classify sentiment with robust model based
ensemble methods. We preprocess tweet data to enhance coverage of tokenizer. To
reduce domain bias, we first train tweet dataset for pre-trained language
model. Besides, each classifier has its strengths and weakness, we leverage
different types of models with ensemble methods: average and power weighted
sum. From the experiments, we show that our approach has achieved positive
effect for sentiment classification. Our system reached third place among 26
teams from the evaluation in SocialNLP 2020 EmotionGIF competition.
| 2,020 | Computation and Language |
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