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Char-RNN for Word Stress Detection in East Slavic Languages | We explore how well a sequence labeling approach, namely, recurrent neural
network, is suited for the task of resource-poor and POS tagging free word
stress detection in the Russian, Ukranian, Belarusian languages. We present new
datasets, annotated with the word stress, for the three languages and compare
several RNN models trained on three languages and explore possible applications
of the transfer learning for the task. We show that it is possible to train a
model in a cross-lingual setting and that using additional languages improves
the quality of the results.
| 2,019 | Computation and Language |
AGRR-2019: A Corpus for Gapping Resolution in Russian | This paper provides a comprehensive overview of the gapping dataset for
Russian that consists of 7.5k sentences with gapping (as well as 15k relevant
negative sentences) and comprises data from various genres: news, fiction,
social media and technical texts. The dataset was prepared for the Automatic
Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at
stimulating the development of NLP tools and methods for processing of
ellipsis.
In this paper, we pay special attention to the gapping resolution methods
that were introduced within the shared task as well as an alternative test set
that illustrates that our corpus is a diverse and representative subset of
Russian language gapping sufficient for effective utilization of machine
learning techniques.
| 2,019 | Computation and Language |
Detecting Everyday Scenarios in Narrative Texts | Script knowledge consists of detailed information on everyday activities.
Such information is often taken for granted in text and needs to be inferred by
readers. Therefore, script knowledge is a central component to language
comprehension. Previous work on representing scripts is mostly based on
extensive manual work or limited to scenarios that can be found with sufficient
redundancy in large corpora. We introduce the task of scenario detection, in
which we identify references to scripts. In this task, we address a wide range
of different scripts (200 scenarios) and we attempt to identify all references
to them in a collection of narrative texts. We present a first benchmark data
set and a baseline model that tackles scenario detection using techniques from
topic segmentation and text classification.
| 2,019 | Computation and Language |
Neural Keyphrase Generation via Reinforcement Learning with Adaptive
Rewards | Generating keyphrases that summarize the main points of a document is a
fundamental task in natural language processing. Although existing generative
models are capable of predicting multiple keyphrases for an input document as
well as determining the number of keyphrases to generate, they still suffer
from the problem of generating too few keyphrases. To address this problem, we
propose a reinforcement learning (RL) approach for keyphrase generation, with
an adaptive reward function that encourages a model to generate both sufficient
and accurate keyphrases. Furthermore, we introduce a new evaluation method that
incorporates name variations of the ground-truth keyphrases using the Wikipedia
knowledge base. Thus, our evaluation method can more robustly evaluate the
quality of predicted keyphrases. Extensive experiments on five real-world
datasets of different scales demonstrate that our RL approach consistently and
significantly improves the performance of the state-of-the-art generative
models with both conventional and new evaluation methods.
| 2,019 | Computation and Language |
Modeling Noisiness to Recognize Named Entities using Multitask Neural
Networks on Social Media | Recognizing named entities in a document is a key task in many NLP
applications. Although current state-of-the-art approaches to this task reach a
high performance on clean text (e.g. newswire genres), those algorithms
dramatically degrade when they are moved to noisy environments such as social
media domains. We present two systems that address the challenges of processing
social media data using character-level phonetics and phonology, word
embeddings, and Part-of-Speech tags as features. The first model is a multitask
end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random
Field (CRF) network whose output layer contains two CRF classifiers. The second
model uses a multitask BLSTM network as feature extractor that transfers the
learning to a CRF classifier for the final prediction. Our systems outperform
the current F1 scores of the state of the art on the Workshop on Noisy
User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more
suitable approach for social media environments.
| 2,018 | Computation and Language |
A Multi-task Approach for Named Entity Recognition in Social Media Data | Named Entity Recognition for social media data is challenging because of its
inherent noisiness. In addition to improper grammatical structures, it contains
spelling inconsistencies and numerous informal abbreviations. We propose a
novel multi-task approach by employing a more general secondary task of Named
Entity (NE) segmentation together with the primary task of fine-grained NE
categorization. The multi-task neural network architecture learns higher order
feature representations from word and character sequences along with basic
Part-of-Speech tags and gazetteer information. This neural network acts as a
feature extractor to feed a Conditional Random Fields classifier. We were able
to obtain the first position in the 3rd Workshop on Noisy User-generated Text
(WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.
| 2,017 | Computation and Language |
Named Entity Recognition on Code-Switched Data: Overview of the CALCS
2018 Shared Task | In the third shared task of the Computational Approaches to Linguistic
Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on
code-switched social-media data. We divide the shared task into two
competitions based on the English-Spanish (ENG-SPA) and Modern Standard
Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity
types to establish a new dataset for code-switched NER benchmarks. In addition
to the CS phenomenon, the diversity of the entities and the social media
challenges make the task considerably hard to process. As a result, the best
scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY,
respectively. We present the scores of 9 participants and discuss the most
common challenges among submissions.
| 2,018 | Computation and Language |
FAKTA: An Automatic End-to-End Fact Checking System | We present FAKTA which is a unified framework that integrates various
components of a fact checking process: document retrieval from media sources
with various types of reliability, stance detection of documents with respect
to given claims, evidence extraction, and linguistic analysis. FAKTA predicts
the factuality of given claims and provides evidence at the document and
sentence level to explain its predictions
| 2,019 | Computation and Language |
Leveraging BERT for Extractive Text Summarization on Lectures | In the last two decades, automatic extractive text summarization on lectures
has demonstrated to be a useful tool for collecting key phrases and sentences
that best represent the content. However, many current approaches utilize dated
approaches, producing sub-par outputs or requiring several hours of manual
tuning to produce meaningful results. Recently, new machine learning
architectures have provided mechanisms for extractive summarization through the
clustering of output embeddings from deep learning models. This paper reports
on the project called Lecture Summarization Service, a python based RESTful
service that utilizes the BERT model for text embeddings and KMeans clustering
to identify sentences closes to the centroid for summary selection. The purpose
of the service was to provide students a utility that could summarize lecture
content, based on their desired number of sentences. On top of the summary
work, the service also includes lecture and summary management, storing content
on the cloud which can be used for collaboration. While the results of
utilizing BERT for extractive summarization were promising, there were still
areas where the model struggled, providing feature research opportunities for
further improvement.
| 2,019 | Computation and Language |
Estimating Causal Effects of Tone in Online Debates | Statistical methods applied to social media posts shed light on the dynamics
of online dialogue. For example, users' wording choices predict their
persuasiveness and users adopt the language patterns of other dialogue
participants. In this paper, we estimate the causal effect of reply tones in
debates on linguistic and sentiment changes in subsequent responses. The
challenge for this estimation is that a reply's tone and subsequent responses
are confounded by the users' ideologies on the debate topic and their emotions.
To overcome this challenge, we learn representations of ideology using
generative models of text. We study debates from 4Forums and compare annotated
tones of replying such as emotional versus factual, or reasonable versus
attacking. We show that our latent confounder representation reduces bias in
ATE estimation. Our results suggest that factual and asserting tones affect
dialogue and provide a methodology for estimating causal effects from text.
| 2,019 | Computation and Language |
Label-Agnostic Sequence Labeling by Copying Nearest Neighbors | Retrieve-and-edit based approaches to structured prediction, where structures
associated with retrieved neighbors are edited to form new structures, have
recently attracted increased interest. However, much recent work merely
conditions on retrieved structures (e.g., in a sequence-to-sequence framework),
rather than explicitly manipulating them. We show we can perform accurate
sequence labeling by explicitly (and only) copying labels from retrieved
neighbors. Moreover, because this copying is label-agnostic, we can achieve
impressive performance when transferring to new sequence-labeling tasks without
retraining. We additionally consider a dynamic programming approach to sequence
labeling in the presence of retrieved neighbors, which allows for controlling
the number of distinct (copied) segments used to form a prediction, and leads
to both more interpretable and accurate predictions.
| 2,021 | Computation and Language |
Psycholinguistics meets Continual Learning: Measuring Catastrophic
Forgetting in Visual Question Answering | We study the issue of catastrophic forgetting in the context of neural
multimodal approaches to Visual Question Answering (VQA). Motivated by evidence
from psycholinguistics, we devise a set of linguistically-informed VQA tasks,
which differ by the types of questions involved (Wh-questions and polar
questions). We test what impact task difficulty has on continual learning, and
whether the order in which a child acquires question types facilitates
computational models. Our results show that dramatic forgetting is at play and
that task difficulty and order matter. Two well-known current continual
learning methods mitigate the problem only to a limiting degree.
| 2,019 | Computation and Language |
Identifying Visible Actions in Lifestyle Vlogs | We consider the task of identifying human actions visible in online videos.
We focus on the widely spread genre of lifestyle vlogs, which consist of videos
of people performing actions while verbally describing them. Our goal is to
identify if actions mentioned in the speech description of a video are visually
present. We construct a dataset with crowdsourced manual annotations of visible
actions, and introduce a multimodal algorithm that leverages information
derived from visual and linguistic clues to automatically infer which actions
are visible in a video. We demonstrate that our multimodal algorithm
outperforms algorithms based only on one modality at a time.
| 2,021 | Computation and Language |
Analyzing the Structure of Attention in a Transformer Language Model | The Transformer is a fully attention-based alternative to recurrent networks
that has achieved state-of-the-art results across a range of NLP tasks. In this
paper, we analyze the structure of attention in a Transformer language model,
the GPT-2 small pretrained model. We visualize attention for individual
instances and analyze the interaction between attention and syntax over a large
corpus. We find that attention targets different parts of speech at different
layer depths within the model, and that attention aligns with dependency
relations most strongly in the middle layers. We also find that the deepest
layers of the model capture the most distant relationships. Finally, we extract
exemplar sentences that reveal highly specific patterns targeted by particular
attention heads.
| 2,019 | Computation and Language |
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View | Recent studies have consistently given positive hints that morphology is
helpful in enriching word embeddings. In this paper, we argue that Chinese word
embeddings can be substantially enriched by the morphological information
hidden in characters which is reflected not only in strokes order sequentially,
but also in character glyphs spatially. Then, we propose a novel Dual-channel
Word Embedding (DWE) model to realize the joint learning of sequential and
spatial information of characters. Through the evaluation on both word
similarity and word analogy tasks, our model shows its rationality and
superiority in modelling the morphology of Chinese.
| 2,019 | Computation and Language |
Automated Curriculum Learning for Turn-level Spoken Language
Understanding with Weak Supervision | We propose a learning approach for turn-level spoken language understanding,
which facilitates a user to speak one or more utterances compositionally in a
turn for completing a task (e.g., voice ordering). A typical pipelined approach
for these understanding tasks requires non-trivial annotation effort for
developing its multiple components. Also, the pipeline is difficult to port to
a new domain or scale up. To address these problems, we propose an end-to-end
statistical model with weak supervision. We employ randomized beam search with
memory augmentation (RBSMA) to solve complicated problems for which long
promising trajectories are usually difficult to explore. Furthermore,
considering the diversity of problem complexity, we explore automated
curriculum learning (CL) for weak supervision to accelerate exploration and
learning. We evaluate the proposed approach on real-world user logs of a
commercial voice ordering system. Results demonstrate that when trained on a
small number of end-to-end annotated sessions collected with low cost, our
model performs comparably to the deployed pipelined system, saving the
development labor over an order of magnitude. The RBSMA algorithm improves the
test set accuracy by 7.8% relative compared to the standard beam search.
Automated CL leads to better generalization and further improves the test set
accuracy by 5% relative.
| 2,019 | Computation and Language |
Word-level Speech Recognition with a Letter to Word Encoder | We propose a direct-to-word sequence model which uses a word network to learn
word embeddings from letters. The word network can be integrated seamlessly
with arbitrary sequence models including Connectionist Temporal Classification
and encoder-decoder models with attention. We show our direct-to-word model can
achieve word error rate gains over sub-word level models for speech
recognition. We also show that our direct-to-word approach retains the ability
to predict words not seen at training time without any retraining. Finally, we
demonstrate that a word-level model can use a larger stride than a sub-word
level model while maintaining accuracy. This makes the model more efficient
both for training and inference.
| 2,020 | Computation and Language |
Federated Learning for Emoji Prediction in a Mobile Keyboard | We show that a word-level recurrent neural network can predict emoji from
text typed on a mobile keyboard. We demonstrate the usefulness of transfer
learning for predicting emoji by pretraining the model using a language
modeling task. We also propose mechanisms to trigger emoji and tune the
diversity of candidates. The model is trained using a distributed on-device
learning framework called federated learning. The federated model is shown to
achieve better performance than a server-trained model. This work demonstrates
the feasibility of using federated learning to train production-quality models
for natural language understanding tasks while keeping users' data on their
devices.
| 2,019 | Computation and Language |
Parallel Scheduled Sampling | Auto-regressive models are widely used in sequence generation problems. The
output sequence is typically generated in a predetermined order, one discrete
unit (pixel or word or character) at a time. The models are trained by
teacher-forcing where ground-truth history is fed to the model as input, which
at test time is replaced by the model prediction. Scheduled Sampling aims to
mitigate this discrepancy between train and test time by randomly replacing
some discrete units in the history with the model's prediction. While
teacher-forced training works well with ML accelerators as the computation can
be parallelized across time, Scheduled Sampling involves undesirable sequential
processing. In this paper, we introduce a simple technique to parallelize
Scheduled Sampling across time. Experimentally, we find the proposed technique
leads to equivalent or better performance on image generation, summarization,
dialog generation, and translation compared to teacher-forced training. In
dialog response generation task, Parallel Scheduled Sampling achieves 1.6 BLEU
score (11.5%) improvement over teacher-forcing while in image generation it
achieves 20% and 13.8% improvement in Frechet Inception Distance (FID) and
Inception Score (IS) respectively. Further, we discuss the effects of different
hyper-parameters associated with Scheduled Sampling on the model performance.
| 2,019 | Computation and Language |
What Does BERT Look At? An Analysis of BERT's Attention | Large pre-trained neural networks such as BERT have had great recent success
in NLP, motivating a growing body of research investigating what aspects of
language they are able to learn from unlabeled data. Most recent analysis has
focused on model outputs (e.g., language model surprisal) or internal vector
representations (e.g., probing classifiers). Complementary to these works, we
propose methods for analyzing the attention mechanisms of pre-trained models
and apply them to BERT. BERT's attention heads exhibit patterns such as
attending to delimiter tokens, specific positional offsets, or broadly
attending over the whole sentence, with heads in the same layer often
exhibiting similar behaviors. We further show that certain attention heads
correspond well to linguistic notions of syntax and coreference. For example,
we find heads that attend to the direct objects of verbs, determiners of nouns,
objects of prepositions, and coreferent mentions with remarkably high accuracy.
Lastly, we propose an attention-based probing classifier and use it to further
demonstrate that substantial syntactic information is captured in BERT's
attention.
| 2,019 | Computation and Language |
A Document-grounded Matching Network for Response Selection in
Retrieval-based Chatbots | We present a document-grounded matching network (DGMN) for response selection
that can power a knowledge-aware retrieval-based chatbot system. The challenges
of building such a model lie in how to ground conversation contexts with
background documents and how to recognize important information in the
documents for matching. To overcome the challenges, DGMN fuses information in a
document and a context into representations of each other, and dynamically
determines if grounding is necessary and importance of different parts of the
document and the context through hierarchical interaction with a response at
the matching step. Empirical studies on two public data sets indicate that DGMN
can significantly improve upon state-of-the-art methods and at the same time
enjoys good interpretability.
| 2,019 | Computation and Language |
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for
Natural Language Understanding in the Medical Domain | This paper describes our competing system to enter the MEDIQA-2019
competition. We use a multi-source transfer learning approach to transfer the
knowledge from MT-DNN and SciBERT to natural language understanding tasks in
the medical domain. For transfer learning fine-tuning, we use multi-task
learning on NLI, RQE and QA tasks on general and medical domains to improve
performance. The proposed methods are proved effective for natural language
understanding in the medical domain, and we rank the first place on the QA
task.
| 2,019 | Computation and Language |
Lightweight and Efficient Neural Natural Language Processing with
Quaternion Networks | Many state-of-the-art neural models for NLP are heavily parameterized and
thus memory inefficient. This paper proposes a series of lightweight and memory
efficient neural architectures for a potpourri of natural language processing
(NLP) tasks. To this end, our models exploit computation using Quaternion
algebra and hypercomplex spaces, enabling not only expressive inter-component
interactions but also significantly ($75\%$) reduced parameter size due to
lesser degrees of freedom in the Hamilton product. We propose Quaternion
variants of models, giving rise to new architectures such as the Quaternion
attention Model and Quaternion Transformer. Extensive experiments on a battery
of NLP tasks demonstrates the utility of proposed Quaternion-inspired models,
enabling up to $75\%$ reduction in parameter size without significant loss in
performance.
| 2,019 | Computation and Language |
Learning a Matching Model with Co-teaching for Multi-turn Response
Selection in Retrieval-based Dialogue Systems | We study learning of a matching model for response selection in
retrieval-based dialogue systems. The problem is equally important with
designing the architecture of a model, but is less explored in existing
literature. To learn a robust matching model from noisy training data, we
propose a general co-teaching framework with three specific teaching strategies
that cover both teaching with loss functions and teaching with data curriculum.
Under the framework, we simultaneously learn two matching models with
independent training sets. In each iteration, one model transfers the knowledge
learned from its training set to the other model, and at the same time receives
the guide from the other model on how to overcome noise in training. Through
being both a teacher and a student, the two models learn from each other and
get improved together. Evaluation results on two public data sets indicate that
the proposed learning approach can generally and significantly improve the
performance of existing matching models.
| 2,019 | Computation and Language |
Reinforcement Learning of Minimalist Numeral Grammars | Speech-controlled user interfaces facilitate the operation of devices and
household functions to laymen. State-of-the-art language technology scans the
acoustically analyzed speech signal for relevant keywords that are subsequently
inserted into semantic slots to interpret the user's intent. In order to
develop proper cognitive information and communication technologies, simple
slot-filling should be replaced by utterance meaning transducers (UMT) that are
based on semantic parsers and a \emph{mental lexicon}, comprising syntactic,
phonetic and semantic features of the language under consideration. This
lexicon must be acquired by a cognitive agent during interaction with its
users. We outline a reinforcement learning algorithm for the acquisition of the
syntactic morphology and arithmetic semantics of English numerals, based on
minimalist grammar (MG), a recent computational implementation of generative
linguistics. Number words are presented to the agent by a teacher in form of
utterance meaning pairs (UMP) where the meanings are encoded as arithmetic
terms from a suitable term algebra. Since MG encodes universal linguistic
competence through inference rules, thereby separating innate linguistic
knowledge from the contingently acquired lexicon, our approach unifies
generative grammar and reinforcement learning, hence potentially resolving the
still pending Chomsky-Skinner controversy.
| 2,019 | Computation and Language |
Self-Supervised Learning for Contextualized Extractive Summarization | Existing models for extractive summarization are usually trained from scratch
with a cross-entropy loss, which does not explicitly capture the global context
at the document level. In this paper, we aim to improve this task by
introducing three auxiliary pre-training tasks that learn to capture the
document-level context in a self-supervised fashion. Experiments on the
widely-used CNN/DM dataset validate the effectiveness of the proposed auxiliary
tasks. Furthermore, we show that after pre-training, a clean model with simple
building blocks is able to outperform previous state-of-the-art that are
carefully designed.
| 2,019 | Computation and Language |
Modeling Sentiment Dependencies with Graph Convolutional Networks for
Aspect-level Sentiment Classification | Aspect-level sentiment classification aims to distinguish the sentiment
polarities over one or more aspect terms in a sentence. Existing approaches
mostly model different aspects in one sentence independently, which ignore the
sentiment dependencies between different aspects. However, we find such
dependency information between different aspects can bring additional valuable
information. In this paper, we propose a novel aspect-level sentiment
classification model based on graph convolutional networks (GCN) which can
effectively capture the sentiment dependencies between multi-aspects in one
sentence. Our model firstly introduces bidirectional attention mechanism with
position encoding to model aspect-specific representations between each aspect
and its context words, then employs GCN over the attention mechanism to capture
the sentiment dependencies between different aspects in one sentence. We
evaluate the proposed approach on the SemEval 2014 datasets. Experiments show
that our model outperforms the state-of-the-art methods. We also conduct
experiments to evaluate the effectiveness of GCN module, which indicates that
the dependencies between different aspects is highly helpful in aspect-level
sentiment classification.
| 2,019 | Computation and Language |
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in
Languages with Rich Morphology | Gender stereotypes are manifest in most of the world's languages and are
consequently propagated or amplified by NLP systems. Although research has
focused on mitigating gender stereotypes in English, the approaches that are
commonly employed produce ungrammatical sentences in morphologically rich
languages. We present a novel approach for converting between
masculine-inflected and feminine-inflected sentences in such languages. For
Spanish and Hebrew, our approach achieves F1 scores of 82% and 73% at the level
of tags and accuracies of 90% and 87% at the level of forms. By evaluating our
approach using four different languages, we show that, on average, it reduces
gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.
| 2,020 | Computation and Language |
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading
Comprehension | This paper considers the reading comprehension task in which multiple
documents are given as input. Prior work has shown that a pipeline of
retriever, reader, and reranker can improve the overall performance. However,
the pipeline system is inefficient since the input is re-encoded within each
module, and is unable to leverage upstream components to help downstream
training. In this work, we present RE$^3$QA, a unified question answering model
that combines context retrieving, reading comprehension, and answer reranking
to predict the final answer. Unlike previous pipelined approaches, RE$^3$QA
shares contextualized text representation across different components, and is
carefully designed to use high-quality upstream outputs (e.g., retrieved
context or candidate answers) for directly supervising downstream modules
(e.g., the reader or the reranker). As a result, the whole network can be
trained end-to-end to avoid the context inconsistency problem. Experiments show
that our model outperforms the pipelined baseline and achieves state-of-the-art
results on two versions of TriviaQA and two variants of SQuAD.
| 2,019 | Computation and Language |
Journal Name Extraction from Japanese Scientific News Articles | In Japanese scientific news articles, although the research results are
described clearly, the article's sources tend to be uncited. This makes it
difficult for readers to know the details of the research. In this paper, we
address the task of extracting journal names from Japanese scientific news
articles. We hypothesize that a journal name is likely to occur in a specific
context. To support the hypothesis, we construct a character-based method and
extract journal names using this method. This method only uses the left and
right context features of journal names. The results of the journal name
extractions suggest that the distribution hypothesis plays an important role in
identifying the journal names.
| 2,019 | Computation and Language |
Inter-sentence Relation Extraction with Document-level Graph
Convolutional Neural Network | Inter-sentence relation extraction deals with a number of complex semantic
relationships in documents, which require local, non-local, syntactic and
semantic dependencies. Existing methods do not fully exploit such dependencies.
We present a novel inter-sentence relation extraction model that builds a
labelled edge graph convolutional neural network model on a document-level
graph. The graph is constructed using various inter- and intra-sentence
dependencies to capture local and non-local dependency information. In order to
predict the relation of an entity pair, we utilise multi-instance learning with
bi-affine pairwise scoring. Experimental results show that our model achieves
comparable performance to the state-of-the-art neural models on two
biochemistry datasets. Our analysis shows that all the types in the graph are
effective for inter-sentence relation extraction.
| 2,019 | Computation and Language |
Generating Summaries with Topic Templates and Structured Convolutional
Decoders | Existing neural generation approaches create multi-sentence text as a single
sequence. In this paper we propose a structured convolutional decoder that is
guided by the content structure of target summaries. We compare our model with
existing sequential decoders on three data sets representing different domains.
Automatic and human evaluation demonstrate that our summaries have better
content coverage.
| 2,019 | Computation and Language |
HEAD-QA: A Healthcare Dataset for Complex Reasoning | We present HEAD-QA, a multi-choice question answering testbed to encourage
research on complex reasoning. The questions come from exams to access a
specialized position in the Spanish healthcare system, and are challenging even
for highly specialized humans. We then consider monolingual (Spanish) and
cross-lingual (to English) experiments with information retrieval and neural
techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the
results lag well behind human performance, demonstrating its usefulness as a
benchmark for future work.
| 2,019 | Computation and Language |
Using Structured Representation and Data: A Hybrid Model for Negation
and Sentiment in Customer Service Conversations | Twitter customer service interactions have recently emerged as an effective
platform to respond and engage with customers. In this work, we explore the
role of negation in customer service interactions, particularly applied to
sentiment analysis. We define rules to identify true negation cues and scope
more suited to conversational data than existing general review data. Using
semantic knowledge and syntactic structure from constituency parse trees, we
propose an algorithm for scope detection that performs comparable to state of
the art BiLSTM. We further investigate the results of negation scope detection
for the sentiment prediction task on customer service conversation data using
both a traditional SVM and a Neural Network. We propose an antonym dictionary
based method for negation applied to a CNN-LSTM combination model for sentiment
analysis. Experimental results show that the antonym-based method outperforms
the previous lexicon-based and neural network methods.
| 2,019 | Computation and Language |
What Kind of Language Is Hard to Language-Model? | How language-agnostic are current state-of-the-art NLP tools? Are there some
types of language that are easier to model with current methods? In prior work
(Cotterell et al., 2018) we attempted to address this question for language
modeling, and observed that recurrent neural network language models do not
perform equally well over all the high-resource European languages found in the
Europarl corpus. We speculated that inflectional morphology may be the primary
culprit for the discrepancy. In this paper, we extend these earlier experiments
to cover 69 languages from 13 language families using a multilingual Bible
corpus. Methodologically, we introduce a new paired-sample multiplicative
mixed-effects model to obtain language difficulty coefficients from
at-least-pairwise parallel corpora. In other words, the model is aware of
inter-sentence variation and can handle missing data. Exploiting this model, we
show that "translationese" is not any easier to model than natively written
language in a fair comparison. Trying to answer the question of what features
difficult languages have in common, we try and fail to reproduce our earlier
(Cotterell et al., 2018) observation about morphological complexity and instead
reveal far simpler statistics of the data that seem to drive complexity in a
much larger sample.
| 2,020 | Computation and Language |
Unsupervised Discovery of Gendered Language through Latent-Variable
Modeling | Studying the ways in which language is gendered has long been an area of
interest in sociolinguistics. Studies have explored, for example, the speech of
male and female characters in film and the language used to describe male and
female politicians. In this paper, we aim not to merely study this phenomenon
qualitatively, but instead to quantify the degree to which the language used to
describe men and women is different and, moreover, different in a positive or
negative way. To that end, we introduce a generative latent-variable model that
jointly represents adjective (or verb) choice, with its sentiment, given the
natural gender of a head (or dependent) noun. We find that there are
significant differences between descriptions of male and female nouns and that
these differences align with common gender stereotypes: Positive adjectives
used to describe women are more often related to their bodies than adjectives
used to describe men.
| 2,019 | Computation and Language |
PerspectroScope: A Window to the World of Diverse Perspectives | This work presents PerspectroScope, a web-based system which lets users query
a discussion-worthy natural language claim, and extract and visualize various
perspectives in support or against the claim, along with evidence supporting
each perspective. The system thus lets users explore various perspectives that
could touch upon aspects of the issue at hand.The system is built as a
combination of retrieval engines and learned textual-entailment-like
classifiers built using a few recent developments in natural language
understanding. To make the system more adaptive, expand its coverage, and
improve its decisions over time, our platform employs various mechanisms to get
corrections from the users.
PerspectroScope is available at github.com/CogComp/perspectroscope.
| 2,019 | Computation and Language |
A Systematic Comparison of English Noun Compound Representations | Building meaningful representations of noun compounds is not trivial since
many of them scarcely appear in the corpus. To that end, composition functions
approximate the distributional representation of a noun compound by combining
its constituent distributional vectors. In the more general case, phrase
embeddings have been trained by minimizing the distance between the vectors
representing paraphrases. We compare various types of noun compound
representations, including distributional, compositional, and paraphrase-based
representations, through a series of tasks and analyses, and with an extensive
number of underlying word embeddings. We find that indeed, in most cases,
composition functions produce higher quality representations than
distributional ones, and they improve with computational power. No single
function performs best in all scenarios, suggesting that a joint training
objective may produce improved representations.
| 2,019 | Computation and Language |
Unmasking Bias in News | We present experiments on detecting hyperpartisanship in news using a
'masking' method that allows us to assess the role of style vs. content for the
task at hand. Our results corroborate previous research on this task in that
topic related features yield better results than stylistic ones. We
additionally show that competitive results can be achieved by simply including
higher-length n-grams, which suggests the need to develop more challenging
datasets and tasks that address implicit and more subtle forms of bias.
| 2,019 | Computation and Language |
CogCompTime: A Tool for Understanding Time in Natural Language Text | Automatic extraction of temporal information in text is an important
component of natural language understanding. It involves two basic tasks: (1)
Understanding time expressions that are mentioned explicitly in text (e.g.,
February 27, 1998 or tomorrow), and (2) Understanding temporal information that
is conveyed implicitly via relations. In this paper, we introduce CogCompTime,
a system that has these two important functionalities. It incorporates the most
recent progress, achieves state-of-the-art performance, and is publicly
available.1 We believe that this demo will be useful for multiple time-aware
applications and provide valuable insight for future research in temporal
understanding.
| 2,019 | Computation and Language |
Joint Reasoning for Temporal and Causal Relations | Understanding temporal and causal relations between events is a fundamental
natural language understanding task. Because a cause must be before its effect
in time, temporal and causal relations are closely related and one relation
even dictates the other one in many cases. However, limited attention has been
paid to studying these two relations jointly. This paper presents a joint
inference framework for them using constrained conditional models (CCMs).
Specifically, we formulate the joint problem as an integer linear programming
(ILP) problem, enforcing constraints inherently in the nature of time and
causality. We show that the joint inference framework results in statistically
significant improvement in the extraction of both temporal and causal relations
from text.
| 2,019 | Computation and Language |
A Structured Learning Approach to Temporal Relation Extraction | Identifying temporal relations between events is an essential step towards
natural language understanding. However, the temporal relation between two
events in a story depends on, and is often dictated by, relations among other
events. Consequently, effectively identifying temporal relations between events
is a challenging problem even for human annotators. This paper suggests that it
is important to take these dependencies into account while learning to identify
these relations and proposes a structured learning approach to address this
challenge. As a byproduct, this provides a new perspective on handling missing
relations, a known issue that hurts existing methods. As we show, the proposed
approach results in significant improvements on the two commonly used data sets
for this problem.
| 2,019 | Computation and Language |
Towards Geocoding Spatial Expressions | Imprecise composite location references formed using ad hoc spatial
expressions in English text makes the geocoding task challenging for both
inference and evaluation. Typically such spatial expressions fill in
unestablished areas with new toponyms for finer spatial referents. For example,
the spatial extent of the ad hoc spatial expression "north of" or "50 minutes
away from" in relation to the toponym "Dayton, OH" refers to an ambiguous,
imprecise area, requiring translation from this qualitative representation to a
quantitative one with precise semantics using systems such as WGS84. Here we
highlight the challenges of geocoding such referents and propose a formal
representation that employs background knowledge, semantic approximations and
rules, and fuzzy linguistic variables. We also discuss an appropriate
evaluation technique for the task that is based on human contextualized and
subjective judgment.
| 2,020 | Computation and Language |
Unsupervised Question Answering by Cloze Translation | Obtaining training data for Question Answering (QA) is time-consuming and
resource-intensive, and existing QA datasets are only available for limited
domains and languages. In this work, we explore to what extent high quality
training data is actually required for Extractive QA, and investigate the
possibility of unsupervised Extractive QA. We approach this problem by first
learning to generate context, question and answer triples in an unsupervised
manner, which we then use to synthesize Extractive QA training data
automatically. To generate such triples, we first sample random context
paragraphs from a large corpus of documents and then random noun phrases or
named entity mentions from these paragraphs as answers. Next we convert answers
in context to "fill-in-the-blank" cloze questions and finally translate them
into natural questions. We propose and compare various unsupervised ways to
perform cloze-to-natural question translation, including training an
unsupervised NMT model using non-aligned corpora of natural questions and cloze
questions as well as a rule-based approach. We find that modern QA models can
learn to answer human questions surprisingly well using only synthetic training
data. We demonstrate that, without using the SQuAD training data at all, our
approach achieves 56.4 F1 on SQuAD v1 (64.5 F1 when the answer is a Named
entity mention), outperforming early supervised models.
| 2,019 | Computation and Language |
Incremental Learning from Scratch for Task-Oriented Dialogue Systems | Clarifying user needs is essential for existing task-oriented dialogue
systems. However, in real-world applications, developers can never guarantee
that all possible user demands are taken into account in the design phase.
Consequently, existing systems will break down when encountering unconsidered
user needs. To address this problem, we propose a novel incremental learning
framework to design task-oriented dialogue systems, or for short Incremental
Dialogue System (IDS), without pre-defining the exhaustive list of user needs.
Specifically, we introduce an uncertainty estimation module to evaluate the
confidence of giving correct responses. If there is high confidence, IDS will
provide responses to users. Otherwise, humans will be involved in the dialogue
process, and IDS can learn from human intervention through an online learning
module. To evaluate our method, we propose a new dataset which simulates
unanticipated user needs in the deployment stage. Experiments show that IDS is
robust to unconsidered user actions, and can update itself online by smartly
selecting only the most effective training data, and hence attains better
performance with less annotation cost.
| 2,019 | Computation and Language |
Adversarial Learning of Privacy-Preserving Text Representations for
De-Identification of Medical Records | De-identification is the task of detecting protected health information (PHI)
in medical text. It is a critical step in sanitizing electronic health records
(EHRs) to be shared for research. Automatic de-identification classifierscan
significantly speed up the sanitization process. However, obtaining a large and
diverse dataset to train such a classifier that works wellacross many types of
medical text poses a challenge as privacy laws prohibit the sharing of raw
medical records. We introduce a method to create privacy-preserving shareable
representations of medical text (i.e. they contain no PHI) that does not
require expensive manual pseudonymization. These representations can be shared
between organizations to create unified datasets for training de-identification
models. Our representation allows training a simple LSTM-CRF de-identification
model to an F1 score of 97.4%, which is comparable to a strong baseline that
exposes private information in its representation. A robust, widely available
de-identification classifier based on our representation could potentially
enable studies for which de-identification would otherwise be too costly.
| 2,019 | Computation and Language |
BiSET: Bi-directional Selective Encoding with Template for Abstractive
Summarization | The success of neural summarization models stems from the meticulous
encodings of source articles. To overcome the impediments of limited and
sometimes noisy training data, one promising direction is to make better use of
the available training data by applying filters during summarization. In this
paper, we propose a novel Bi-directional Selective Encoding with Template
(BiSET) model, which leverages template discovered from training data to softly
select key information from each source article to guide its summarization
process. Extensive experiments on a standard summarization dataset were
conducted and the results show that the template-equipped BiSET model manages
to improve the summarization performance significantly with a new state of the
art.
| 2,019 | Computation and Language |
Concept Discovery through Information Extraction in Restaurant Domain | Concept identification is a crucial step in understanding and building a
knowledge base for any particular domain. However, it is not a simple task in
very large domains such as restaurants and hotel. In this paper, a novel
approach of identifying a concept hierarchy and classifying unseen words into
identified concepts related to restaurant domain is presented. Sorting,
identifying, classifying of domain-related words manually is tedious and
therefore, the proposed process is automated to a great extent. Word embedding,
hierarchical clustering, classification algorithms are effectively used to
obtain concepts related to the restaurant domain. Further, this approach can
also be extended to create a semi-automatic ontology on restaurant domain.
| 2,019 | Computation and Language |
Probing Multilingual Sentence Representations With X-Probe | This paper extends the task of probing sentence representations for
linguistic insight in a multilingual domain. In doing so, we make two
contributions: first, we provide datasets for multilingual probing, derived
from Wikipedia, in five languages, viz. English, French, German, Spanish and
Russian. Second, we evaluate six sentence encoders for each language, each
trained by mapping sentence representations to English sentence
representations, using sentences in a parallel corpus. We discover that
cross-lingually mapped representations are often better at retaining certain
linguistic information than representations derived from English encoders
trained on natural language inference (NLI) as a downstream task.
| 2,019 | Computation and Language |
Unified Semantic Parsing with Weak Supervision | Semantic parsing over multiple knowledge bases enables a parser to exploit
structural similarities of programs across the multiple domains. However, the
fundamental challenge lies in obtaining high-quality annotations of (utterance,
program) pairs across various domains needed for training such models. To
overcome this, we propose a novel framework to build a unified multi-domain
enabled semantic parser trained only with weak supervision (denotations).
Weakly supervised training is particularly arduous as the program search space
grows exponentially in a multi-domain setting. To solve this, we incorporate a
multi-policy distillation mechanism in which we first train domain-specific
semantic parsers (teachers) using weak supervision in the absence of the ground
truth programs, followed by training a single unified parser (student) from the
domain specific policies obtained from these teachers. The resultant semantic
parser is not only compact but also generalizes better, and generates more
accurate programs. It further does not require the user to provide a domain
label while querying. On the standard Overnight dataset (containing multiple
domains), we demonstrate that the proposed model improves performance by 20% in
terms of denotation accuracy in comparison to baseline techniques.
| 2,019 | Computation and Language |
Putting words in context: LSTM language models and lexical ambiguity | In neural network models of language, words are commonly represented using
context-invariant representations (word embeddings) which are then put in
context in the hidden layers. Since words are often ambiguous, representing the
contextually relevant information is not trivial. We investigate how an LSTM
language model deals with lexical ambiguity in English, designing a method to
probe its hidden representations for lexical and contextual information about
words. We find that both types of information are represented to a large
extent, but also that there is room for improvement for contextual information.
| 2,019 | Computation and Language |
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop
Reading Comprehension | Multi-hop reading comprehension requires the model to explore and connect
relevant information from multiple sentences/documents in order to answer the
question about the context. To achieve this, we propose an interpretable
3-module system called Explore-Propose-Assemble reader (EPAr). First, the
Document Explorer iteratively selects relevant documents and represents
divergent reasoning chains in a tree structure so as to allow assimilating
information from all chains. The Answer Proposer then proposes an answer from
every root-to-leaf path in the reasoning tree. Finally, the Evidence Assembler
extracts a key sentence containing the proposed answer from every path and
combines them to predict the final answer. Intuitively, EPAr approximates the
coarse-to-fine-grained comprehension behavior of human readers when facing
multiple long documents. We jointly optimize our 3 modules by minimizing the
sum of losses from each stage conditioned on the previous stage's output. On
two multi-hop reading comprehension datasets WikiHop and MedHop, our EPAr model
achieves significant improvements over the baseline and competitive results
compared to the state-of-the-art model. We also present multiple
reasoning-chain-recovery tests and ablation studies to demonstrate our system's
ability to perform interpretable and accurate reasoning.
| 2,019 | Computation and Language |
Monotonic Infinite Lookback Attention for Simultaneous Machine
Translation | Simultaneous machine translation begins to translate each source sentence
before the source speaker is finished speaking, with applications to live and
streaming scenarios. Simultaneous systems must carefully schedule their reading
of the source sentence to balance quality against latency. We present the first
simultaneous translation system to learn an adaptive schedule jointly with a
neural machine translation (NMT) model that attends over all source tokens read
thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention,
which maintains both a hard, monotonic attention head to schedule the reading
of the source sentence, and a soft attention head that extends from the
monotonic head back to the beginning of the source. We show that MILk's
adaptive schedule allows it to arrive at latency-quality trade-offs that are
favorable to those of a recently proposed wait-k strategy for many latency
values.
| 2,019 | Computation and Language |
Continual and Multi-Task Architecture Search | Architecture search is the process of automatically learning the neural model
or cell structure that best suits the given task. Recently, this approach has
shown promising performance improvements (on language modeling and image
classification) with reasonable training speed, using a weight sharing strategy
called Efficient Neural Architecture Search (ENAS). In our work, we first
introduce a novel continual architecture search (CAS) approach, so as to
continually evolve the model parameters during the sequential training of
several tasks, without losing performance on previously learned tasks (via
block-sparsity and orthogonality constraints), thus enabling life-long
learning. Next, we explore a multi-task architecture search (MAS) approach over
ENAS for finding a unified, single cell structure that performs well across
multiple tasks (via joint controller rewards), and hence allows more
generalizable transfer of the cell structure knowledge to an unseen new task.
We empirically show the effectiveness of our sequential continual learning and
parallel multi-task learning based architecture search approaches on diverse
sentence-pair classification tasks (GLUE) and multimodal-generation based video
captioning tasks. Further, we present several ablations and analyses on the
learned cell structures.
| 2,019 | Computation and Language |
Keeping Notes: Conditional Natural Language Generation with a Scratchpad
Mechanism | We introduce the Scratchpad Mechanism, a novel addition to the
sequence-to-sequence (seq2seq) neural network architecture and demonstrate its
effectiveness in improving the overall fluency of seq2seq models for natural
language generation tasks. By enabling the decoder at each time step to write
to all of the encoder output layers, Scratchpad can employ the encoder as a
"scratchpad" memory to keep track of what has been generated so far and thereby
guide future generation. We evaluate Scratchpad in the context of three
well-studied natural language generation tasks --- Machine Translation,
Question Generation, and Text Summarization --- and obtain state-of-the-art or
comparable performance on standard datasets for each task. Qualitative
assessments in the form of human judgements (question generation), attention
visualization (MT), and sample output (summarization) provide further evidence
of the ability of Scratchpad to generate fluent and expressive output.
| 2,019 | Computation and Language |
COMET: Commonsense Transformers for Automatic Knowledge Graph
Construction | We present the first comprehensive study on automatic knowledge base
construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et
al., 2019) and ConceptNet (Speer et al., 2017). Contrary to many conventional
KBs that store knowledge with canonical templates, commonsense KBs only store
loosely structured open-text descriptions of knowledge. We posit that an
important step toward automatic commonsense completion is the development of
generative models of commonsense knowledge, and propose COMmonsEnse
Transformers (COMET) that learn to generate rich and diverse commonsense
descriptions in natural language. Despite the challenges of commonsense
modeling, our investigation reveals promising results when implicit knowledge
from deep pre-trained language models is transferred to generate explicit
knowledge in commonsense knowledge graphs. Empirical results demonstrate that
COMET is able to generate novel knowledge that humans rate as high quality,
with up to 77.5% (ATOMIC) and 91.7% (ConceptNet) precision at top 1, which
approaches human performance for these resources. Our findings suggest that
using generative commonsense models for automatic commonsense KB completion
could soon be a plausible alternative to extractive methods.
| 2,019 | Computation and Language |
E3: Entailment-driven Extracting and Editing for Conversational Machine
Reading | Conversational machine reading systems help users answer high-level questions
(e.g. determine if they qualify for particular government benefits) when they
do not know the exact rules by which the determination is made(e.g. whether
they need certain income levels or veteran status). The key challenge is that
these rules are only provided in the form of a procedural text (e.g. guidelines
from government website) which the system must read to figure out what to ask
the user. We present a new conversational machine reading model that jointly
extracts a set of decision rules from the procedural text while reasoning about
which are entailed by the conversational history and which still need to be
edited to create questions for the user. On the recently introduced ShARC
conversational machine reading dataset, our Entailment-driven Extract and Edit
network (E3) achieves a new state-of-the-art, outperforming existing systems as
well as a new BERT-based baseline. In addition, by explicitly highlighting
which information still needs to be gathered, E3 provides a more explainable
alternative to prior work. We release source code for our models and
experiments at https://github.com/vzhong/e3.
| 2,020 | Computation and Language |
Compositional generalization through meta sequence-to-sequence learning | People can learn a new concept and use it compositionally, understanding how
to "blicket twice" after learning how to "blicket." In contrast, powerful
sequence-to-sequence (seq2seq) neural networks fail such tests of
compositionality, especially when composing new concepts together with existing
concepts. In this paper, I show how memory-augmented neural networks can be
trained to generalize compositionally through meta seq2seq learning. In this
approach, models train on a series of seq2seq problems to acquire the
compositional skills needed to solve new seq2seq problems. Meta se2seq learning
solves several of the SCAN tests for compositional learning and can learn to
apply implicit rules to variables.
| 2,019 | Computation and Language |
Neural Arabic Question Answering | This paper tackles the problem of open domain factual Arabic question
answering (QA) using Wikipedia as our knowledge source. This constrains the
answer of any question to be a span of text in Wikipedia. Open domain QA for
Arabic entails three challenges: annotated QA datasets in Arabic, large scale
efficient information retrieval and machine reading comprehension. To deal with
the lack of Arabic QA datasets we present the Arabic Reading Comprehension
Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia
articles, and a machine translation of the Stanford Question Answering Dataset
(Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL)
is based on two components: (1) a document retriever using a hierarchical
TF-IDF approach and (2) a neural reading comprehension model using the
pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate
the effectiveness of our approach with our BERT-based reader achieving a 61.3
F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.
| 2,019 | Computation and Language |
Analyzing the Limitations of Cross-lingual Word Embedding Mappings | Recent research in cross-lingual word embeddings has almost exclusively
focused on offline methods, which independently train word embeddings in
different languages and map them to a shared space through linear
transformations. While several authors have questioned the underlying
isomorphism assumption, which states that word embeddings in different
languages have approximately the same structure, it is not clear whether this
is an inherent limitation of mapping approaches or a more general issue when
learning cross-lingual embeddings. So as to answer this question, we experiment
with parallel corpora, which allows us to compare offline mapping to an
extension of skip-gram that jointly learns both embedding spaces. We observe
that, under these ideal conditions, joint learning yields to more isomorphic
embeddings, is less sensitive to hubness, and obtains stronger results in
bilingual lexicon induction. We thus conclude that current mapping methods do
have strong limitations, calling for further research to jointly learn
cross-lingual embeddings with a weaker cross-lingual signal.
| 2,021 | Computation and Language |
Synthetic QA Corpora Generation with Roundtrip Consistency | We introduce a novel method of generating synthetic question answering
corpora by combining models of question generation and answer extraction, and
by filtering the results to ensure roundtrip consistency. By pretraining on the
resulting corpora we obtain significant improvements on SQuAD2 and NQ,
establishing a new state-of-the-art on the latter. Our synthetic data
generation models, for both question generation and answer extraction, can be
fully reproduced by finetuning a publicly available BERT model on the
extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant
that does full sequence-to-sequence pretraining for question generation,
obtaining exact match and F1 at less than 0.1% and 0.4% from human performance
on SQuAD2.
| 2,019 | Computation and Language |
Cued@wmt19:ewc&lms | Two techniques provide the fabric of the Cambridge University Engineering
Department's (CUED) entry to the WMT19 evaluation campaign: elastic weight
consolidation (EWC) and different forms of language modelling (LMs). We report
substantial gains by fine-tuning very strong baselines on former WMT test sets
using a combination of checkpoint averaging and EWC. A sentence-level
Transformer LM and a document-level LM based on a modified Transformer
architecture yield further gains. As in previous years, we also extract
$n$-gram probabilities from SMT lattices which can be seen as a
source-conditioned $n$-gram LM.
| 2,019 | Computation and Language |
Figurative Usage Detection of Symptom Words to Improve Personal Health
Mention Detection | Personal health mention detection deals with predicting whether or not a
given sentence is a report of a health condition. Past work mentions errors in
this prediction when symptom words, i.e. names of symptoms of interest, are
used in a figurative sense. Therefore, we combine a state-of-the-art figurative
usage detection with CNN-based personal health mention detection. To do so, we
present two methods: a pipeline-based approach and a feature augmentation-based
approach. The introduction of figurative usage detection results in an average
improvement of 2.21% F-score of personal health mention detection, in the case
of the feature augmentation-based approach. This paper demonstrates the promise
of using figurative usage detection to improve personal health mention
detection.
| 2,019 | Computation and Language |
A Comparison of Word-based and Context-based Representations for
Classification Problems in Health Informatics | Distributed representations of text can be used as features when training a
statistical classifier. These representations may be created as a composition
of word vectors or as context-based sentence vectors. We compare the two kinds
of representations (word versus context) for three classification problems:
influenza infection classification, drug usage classification and personal
health mention classification. For statistical classifiers trained for each of
these problems, context-based representations based on ELMo, Universal Sentence
Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe
and the two adapted using the MESH ontology. There is an improvement of 2-4% in
the accuracy when these context-based representations are used instead of
word-based representations.
| 2,019 | Computation and Language |
Transfer Learning in Biomedical Natural Language Processing: An
Evaluation of BERT and ELMo on Ten Benchmarking Datasets | Inspired by the success of the General Language Understanding Evaluation
benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE)
benchmark to facilitate research in the development of pre-training language
representations in the biomedicine domain. The benchmark consists of five tasks
with ten datasets that cover both biomedical and clinical texts with different
dataset sizes and difficulties. We also evaluate several baselines based on
BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and
MIMIC-III clinical notes achieves the best results. We make the datasets,
pre-trained models, and codes publicly available at
https://github.com/ncbi-nlp/BLUE_Benchmark.
| 2,019 | Computation and Language |
Enriching Neural Models with Targeted Features for Dementia Detection | Alzheimer's disease (AD) is an irreversible brain disease that can
dramatically reduce quality of life, most commonly manifesting in older adults
and eventually leading to the need for full-time care. Early detection is
fundamental to slowing its progression; however, diagnosis can be expensive,
time-consuming, and invasive. In this work we develop a neural model based on a
CNN-LSTM architecture that learns to detect AD and related dementias using
targeted and implicitly-learned features from conversational transcripts. Our
approach establishes the new state of the art on the DementiaBank dataset,
achieving an F1 score of 0.929 when classifying participants into AD and
control groups.
| 2,019 | Computation and Language |
A Computational Analysis of Natural Languages to Build a Sentence
Structure Aware Artificial Neural Network | Natural languages are complexly structured entities. They exhibit
characterising regularities that can be exploited to link them one another. In
this work, I compare two morphological aspects of languages: Written Patterns
and Sentence Structure. I show how languages spontaneously group by similarity
in both analyses and derive an average language distance. Finally, exploiting
Sentence Structure I developed an Artificial Neural Network capable of
distinguishing languages suggesting that not only word roots but also
grammatical sentence structure is a characterising trait which alone suffice to
identify them.
| 2,019 | Computation and Language |
Character n-gram Embeddings to Improve RNN Language Models | This paper proposes a novel Recurrent Neural Network (RNN) language model
that takes advantage of character information. We focus on character n-grams
based on research in the field of word embedding construction (Wieting et al.
2016). Our proposed method constructs word embeddings from character n-gram
embeddings and combines them with ordinary word embeddings. We demonstrate that
the proposed method achieves the best perplexities on the language modeling
datasets: Penn Treebank, WikiText-2, and WikiText-103. Moreover, we conduct
experiments on application tasks: machine translation and headline generation.
The experimental results indicate that our proposed method also positively
affects these tasks.
| 2,019 | Computation and Language |
Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to
Objects of Unknown Categories | Zero-shot learning in Language & Vision is the task of correctly labelling
(or naming) objects of novel categories. Another strand of work in L&V aims at
pragmatically informative rather than ``correct'' object descriptions, e.g. in
reference games. We combine these lines of research and model zero-shot
reference games, where a speaker needs to successfully refer to a novel object
in an image. Inspired by models of "rational speech acts", we extend a neural
generator to become a pragmatic speaker reasoning about uncertain object
categories. As a result of this reasoning, the generator produces fewer nouns
and names of distractor categories as compared to a literal speaker. We show
that this conversational strategy for dealing with novel objects often improves
communicative success, in terms of resolution accuracy of an automatic
listener.
| 2,019 | Computation and Language |
Lattice Transformer for Speech Translation | Recent advances in sequence modeling have highlighted the strengths of the
transformer architecture, especially in achieving state-of-the-art machine
translation results. However, depending on the up-stream systems, e.g., speech
recognition, or word segmentation, the input to translation system can vary
greatly. The goal of this work is to extend the attention mechanism of the
transformer to naturally consume the lattice in addition to the traditional
sequential input. We first propose a general lattice transformer for speech
translation where the input is the output of the automatic speech recognition
(ASR) which contains multiple paths and posterior scores. To leverage the extra
information from the lattice structure, we develop a novel controllable lattice
attention mechanism to obtain latent representations. On the LDC
Spanish-English speech translation corpus, our experiments show that lattice
transformer generalizes significantly better and outperforms both a transformer
baseline and a lattice LSTM. Additionally, we validate our approach on the WMT
2017 Chinese-English translation task with lattice inputs from different BPE
segmentations. In this task, we also observe the improvements over strong
baselines.
| 2,019 | Computation and Language |
Proactive Human-Machine Conversation with Explicit Conversation Goals | Though great progress has been made for human-machine conversation, current
dialogue system is still in its infancy: it usually converses passively and
utters words more as a matter of response, rather than on its own initiatives.
In this paper, we take a radical step towards building a human-like
conversational agent: endowing it with the ability of proactively leading the
conversation (introducing a new topic or maintaining the current topic). To
facilitate the development of such conversation systems, we create a new
dataset named DuConv where one acts as a conversation leader and the other acts
as the follower. The leader is provided with a knowledge graph and asked to
sequentially change the discussion topics, following the given conversation
goal, and meanwhile keep the dialogue as natural and engaging as possible.
DuConv enables a very challenging task as the model needs to both understand
dialogue and plan over the given knowledge graph. We establish baseline results
on this dataset (about 270K utterances and 30k dialogues) using several
state-of-the-art models. Experimental results show that dialogue models that
plan over the knowledge graph can make full use of related knowledge to
generate more diverse multi-turn conversations. The baseline systems along with
the dataset are publicly available
| 2,019 | Computation and Language |
Antonym-Synonym Classification Based on New Sub-space Embeddings | Distinguishing antonyms from synonyms is a key challenge for many NLP
applications focused on the lexical-semantic relation extraction. Existing
solutions relying on large-scale corpora yield low performance because of huge
contextual overlap of antonym and synonym pairs. We propose a novel approach
entirely based on pre-trained embeddings. We hypothesize that the pre-trained
embeddings comprehend a blend of lexical-semantic information and we may
distill the task-specific information using Distiller, a model proposed in this
paper. Later, a classifier is trained based on features constructed from the
distilled sub-spaces along with some word level features to distinguish
antonyms from synonyms. Experimental results show that the proposed model
outperforms existing research on antonym synonym distinction in both speed and
performance.
| 2,019 | Computation and Language |
Representation Learning for Words and Entities | This thesis presents new methods for unsupervised learning of distributed
representations of words and entities from text and knowledge bases. The first
algorithm presented in the thesis is a multi-view algorithm for learning
representations of words called Multiview Latent Semantic Analysis (MVLSA). By
incorporating up to 46 different types of co-occurrence statistics for the same
vocabulary of english words, I show that MVLSA outperforms other
state-of-the-art word embedding models. Next, I focus on learning entity
representations for search and recommendation and present the second method of
this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an
unsupervised learning method, but it is based on the Variational Autoencoder
framework. Evaluations with human annotators show that NVSE can facilitate
better search and recommendation of information gathered from noisy, automatic
annotation of unstructured natural language corpora. Finally, I move from
unstructured data and focus on structured knowledge graphs. I present novel
approaches for learning embeddings of vertices and edges in a knowledge graph
that obey logical constraints.
| 2,019 | Computation and Language |
Deep Two-path Semi-supervised Learning for Fake News Detection | News in social media such as Twitter has been generated in high volume and
speed. However, very few of them can be labeled (as fake or true news) in a
short time. In order to achieve timely detection of fake news in social media,
a novel deep two-path semi-supervised learning model is proposed, where one
path is for supervised learning and the other is for unsupervised learning.
These two paths implemented with convolutional neural networks are jointly
optimized to enhance detection performance. In addition, we build a shared
convolutional neural networks between these two paths to share the low level
features. Experimental results using Twitter datasets show that the proposed
model can recognize fake news effectively with very few labeled data.
| 2,019 | Computation and Language |
Calibration, Entropy Rates, and Memory in Language Models | Building accurate language models that capture meaningful long-term
dependencies is a core challenge in natural language processing. Towards this
end, we present a calibration-based approach to measure long-term discrepancies
between a generative sequence model and the true distribution, and use these
discrepancies to improve the model. Empirically, we show that state-of-the-art
language models, including LSTMs and Transformers, are \emph{miscalibrated}:
the entropy rates of their generations drift dramatically upward over time. We
then provide provable methods to mitigate this phenomenon. Furthermore, we show
how this calibration-based approach can also be used to measure the amount of
memory that language models use for prediction.
| 2,019 | Computation and Language |
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine
Decoding | Generating long and informative review text is a challenging natural language
generation task. Previous work focuses on word-level generation, neglecting the
importance of topical and syntactic characteristics from natural languages. In
this paper, we propose a novel review generation model by characterizing an
elaborately designed aspect-aware coarse-to-fine generation process. First, we
model the aspect transitions to capture the overall content flow. Then, to
generate a sentence, an aspect-aware sketch will be predicted using an
aspect-aware decoder. Finally, another decoder fills in the semantic slots by
generating corresponding words. Our approach is able to jointly utilize aspect
semantics, syntactic sketch, and context information. Extensive experiments
results have demonstrated the effectiveness of the proposed model.
| 2,021 | Computation and Language |
Translating Translationese: A Two-Step Approach to Unsupervised Machine
Translation | Given a rough, word-by-word gloss of a source language sentence, target
language natives can uncover the latent, fully-fluent rendering of the
translation. In this work we explore this intuition by breaking translation
into a two step process: generating a rough gloss by means of a dictionary and
then `translating' the resulting pseudo-translation, or `Translationese' into a
fully fluent translation. We build our Translationese decoder once from a
mish-mash of parallel data that has the target language in common and then can
build dictionaries on demand using unsupervised techniques, resulting in
rapidly generated unsupervised neural MT systems for many source languages. We
apply this process to 14 test languages, obtaining better or comparable
translation results on high-resource languages than previously published
unsupervised MT studies, and obtaining good quality results for low-resource
languages that have never been used in an unsupervised MT scenario.
| 2,019 | Computation and Language |
A Focus on Neural Machine Translation for African Languages | African languages are numerous, complex and low-resourced. The datasets
required for machine translation are difficult to discover, and existing
research is hard to reproduce. Minimal attention has been given to machine
translation for African languages so there is scant research regarding the
problems that arise when using machine translation techniques. To begin
addressing these problems, we trained models to translate English to five of
the official South African languages (Afrikaans, isiZulu, Northern Sotho,
Setswana, Xitsonga), making use of modern neural machine translation
techniques. The results obtained show the promise of using neural machine
translation techniques for African languages. By providing reproducible
publicly-available data, code and results, this research aims to provide a
starting point for other researchers in African machine translation to compare
to and build upon.
| 2,019 | Computation and Language |
Unsupervised Neural Single-Document Summarization of Reviews via
Learning Latent Discourse Structure and its Ranking | This paper focuses on the end-to-end abstractive summarization of a single
product review without supervision. We assume that a review can be described as
a discourse tree, in which the summary is the root, and the child sentences
explain their parent in detail. By recursively estimating a parent from its
children, our model learns the latent discourse tree without an external parser
and generates a concise summary. We also introduce an architecture that ranks
the importance of each sentence on the tree to support summary generation
focusing on the main review point. The experimental results demonstrate that
our model is competitive with or outperforms other unsupervised approaches. In
particular, for relatively long reviews, it achieves a competitive or better
performance than supervised models. The induced tree shows that the child
sentences provide additional information about their parent, and the generated
summary abstracts the entire review.
| 2,019 | Computation and Language |
Improved Sentiment Detection via Label Transfer from Monolingual to
Synthetic Code-Switched Text | Multilingual writers and speakers often alternate between two languages in a
single discourse, a practice called "code-switching". Existing sentiment
detection methods are usually trained on sentiment-labeled monolingual text.
Manually labeled code-switched text, especially involving minority languages,
is extremely rare. Consequently, the best monolingual methods perform
relatively poorly on code-switched text. We present an effective technique for
synthesizing labeled code-switched text from labeled monolingual text, which is
more readily available. The idea is to replace carefully selected subtrees of
constituency parses of sentences in the resource-rich language with suitable
token spans selected from automatic translations to the resource-poor language.
By augmenting scarce human-labeled code-switched text with plentiful synthetic
code-switched text, we achieve significant improvements in sentiment labeling
accuracy (1.5%, 5.11%, 7.20%) for three different language pairs
(English-Hindi, English-Spanish and English-Bengali). We also get significant
gains for hate speech detection: 4% improvement using only synthetic text and
6% if augmented with real text.
| 2,019 | Computation and Language |
Semantic Change and Semantic Stability: Variation is Key | I survey some recent approaches to studying change in the lexicon,
particularly change in meaning across phylogenies. I briefly sketch an
evolutionary approach to language change and point out some issues in recent
approaches to studying semantic change that rely on temporally stratified word
embeddings. I draw illustrations from lexical cognate models in Pama-Nyungan to
identify meaning classes most appropriate for lexical phylogenetic inference,
particularly highlighting the importance of variation in studying change over
time.
| 2,019 | Computation and Language |
Anti dependency distance minimization in short sequences. A graph
theoretic approach | Dependency distance minimization (DDm) is a word order principle favouring
the placement of syntactically related words close to each other in sentences.
Massive evidence of the principle has been reported for more than a decade with
the help of syntactic dependency treebanks where long sentences abound.
However, it has been predicted theoretically that the principle is more likely
to be beaten in short sequences by the principle of surprisal minimization
(predictability maximization). Here we introduce a simple binomial test to
verify such a hypothesis. In short sentences, we find anti-DDm for some
languages from different families. Our analysis of the syntactic dependency
structures suggests that anti-DDm is produced by star trees.
| 2,021 | Computation and Language |
UCAM Biomedical translation at WMT19: Transfer learning multi-domain
ensembles | The 2019 WMT Biomedical translation task involved translating Medline
abstracts. We approached this using transfer learning to obtain a series of
strong neural models on distinct domains, and combining them into multi-domain
ensembles. We further experiment with an adaptive language-model ensemble
weighting scheme. Our submission achieved the best submitted results on both
directions of English-Spanish.
| 2,019 | Computation and Language |
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index | Existing open-domain question answering (QA) models are not suitable for
real-time usage because they need to process several long documents on-demand
for every input query. In this paper, we introduce the query-agnostic indexable
representation of document phrases that can drastically speed up open-domain QA
and also allows us to reach long-tail targets. In particular, our dense-sparse
phrase encoding effectively captures syntactic, semantic, and lexical
information of the phrases and eliminates the pipeline filtering of context
documents. Leveraging optimization strategies, our model can be trained in a
single 4-GPU server and serve entire Wikipedia (up to 60 billion phrases) under
2TB with CPUs only. Our experiments on SQuAD-Open show that our model is more
accurate than DrQA (Chen et al., 2017) with 6000x reduced computational cost,
which translates into at least 58x faster end-to-end inference benchmark on
CPUs.
| 2,019 | Computation and Language |
Sentiment analysis is not solved! Assessing and probing sentiment
classification | Neural methods for SA have led to quantitative improvements over previous
approaches, but these advances are not always accompanied with a thorough
analysis of the qualitative differences. Therefore, it is not clear what
outstanding conceptual challenges for sentiment analysis remain. In this work,
we attempt to discover what challenges still prove a problem for sentiment
classifiers for English and to provide a challenging dataset. We collect the
subset of sentences that an (oracle) ensemble of state-of-the-art sentiment
classifiers misclassify and then annotate them for 18 linguistic and
paralinguistic phenomena, such as negation, sarcasm, modality, etc. The dataset
is available at https://github.com/ltgoslo/assessing_and_probing_sentiment.
Finally, we provide a case study that demonstrates the usefulness of the
dataset to probe the performance of a given sentiment classifier with respect
to linguistic phenomena.
| 2,019 | Computation and Language |
On the Effect of Word Order on Cross-lingual Sentiment Analysis | Current state-of-the-art models for sentiment analysis make use of word order
either explicitly by pre-training on a language modeling objective or
implicitly by using recurrent neural networks (RNNs) or convolutional networks
(CNNs). This is a problem for cross-lingual models that use bilingual
embeddings as features, as the difference in word order between source and
target languages is not resolved. In this work, we explore reordering as a
pre-processing step for sentence-level cross-lingual sentiment classification
with two language combinations (English-Spanish, English-Catalan). We find that
while reordering helps both models, CNNS are more sensitive to local
reorderings, while global reordering benefits RNNs.
| 2,019 | Computation and Language |
Meaning to Form: Measuring Systematicity as Information | A longstanding debate in semiotics centers on the relationship between
linguistic signs and their corresponding semantics: is there an arbitrary
relationship between a word form and its meaning, or does some systematic
phenomenon pervade? For instance, does the character bigram \textit{gl} have
any systematic relationship to the meaning of words like \textit{glisten},
\textit{gleam} and \textit{glow}? In this work, we offer a holistic
quantification of the systematicity of the sign using mutual information and
recurrent neural networks. We employ these in a data-driven and massively
multilingual approach to the question, examining 106 languages. We find a
statistically significant reduction in entropy when modeling a word form
conditioned on its semantic representation. Encouragingly, we also recover
well-attested English examples of systematic affixes. We conclude with the
meta-point: Our approximate effect size (measured in bits) is quite
small---despite some amount of systematicity between form and meaning, an
arbitrary relationship and its resulting benefits dominate human language.
| 2,019 | Computation and Language |
Conceptor Debiasing of Word Representations Evaluated on WEAT | Bias in word embeddings such as Word2Vec has been widely investigated, and
many efforts made to remove such bias. We show how to use conceptors debiasing
to post-process both traditional and contextualized word embeddings. Our
conceptor debiasing can simultaneously remove racial and gender biases and,
unlike standard debiasing methods, can make effect use of heterogeneous lists
of biased words. We show that conceptor debiasing diminishes racial and gender
bias of word representations as measured using the Word Embedding Association
Test (WEAT) of Caliskan et al. (2017).
| 2,019 | Computation and Language |
Cost-sensitive Regularization for Label Confusion-aware Event Detection | In supervised event detection, most of the mislabeling occurs between a small
number of confusing type pairs, including trigger-NIL pairs and sibling
sub-types of the same coarse type. To address this label confusion problem,
this paper proposes cost-sensitive regularization, which can force the training
procedure to concentrate more on optimizing confusing type pairs. Specifically,
we introduce a cost-weighted term into the training loss, which penalizes more
on mislabeling between confusing label pairs. Furthermore, we also propose two
estimators which can effectively measure such label confusion based on
instance-level or population-level statistics. Experiments on TAC-KBP 2017
datasets demonstrate that the proposed method can significantly improve the
performances of different models in both English and Chinese event detection.
| 2,019 | Computation and Language |
Learning to Ask Unanswerable Questions for Machine Reading Comprehension | Machine reading comprehension with unanswerable questions is a challenging
task. In this work, we propose a data augmentation technique by automatically
generating relevant unanswerable questions according to an answerable question
paired with its corresponding paragraph that contains the answer. We introduce
a pair-to-sequence model for unanswerable question generation, which
effectively captures the interactions between the question and the paragraph.
We also present a way to construct training data for our question generation
models by leveraging the existing reading comprehension dataset. Experimental
results show that the pair-to-sequence model performs consistently better
compared with the sequence-to-sequence baseline. We further use the
automatically generated unanswerable questions as a means of data augmentation
on the SQuAD 2.0 dataset, yielding 1.9 absolute F1 improvement with BERT-base
model and 1.7 absolute F1 improvement with BERT-large model.
| 2,019 | Computation and Language |
Neural Response Generation with Meta-Words | We present open domain response generation with meta-words. A meta-word is a
structured record that describes various attributes of a response, and thus
allows us to explicitly model the one-to-many relationship within open domain
dialogues and perform response generation in an explainable and controllable
manner. To incorporate meta-words into generation, we enhance the
sequence-to-sequence architecture with a goal tracking memory network that
formalizes meta-word expression as a goal and manages the generation process to
achieve the goal with a state memory panel and a state controller. Experimental
results on two large-scale datasets indicate that our model can significantly
outperform several state-of-the-art generation models in terms of response
relevance, response diversity, accuracy of one-to-many modeling, accuracy of
meta-word expression, and human evaluation.
| 2,019 | Computation and Language |
Microsoft AI Challenge India 2018: Learning to Rank Passages for Web
Question Answering with Deep Attention Networks | This paper describes our system for The Microsoft AI Challenge India 2018:
Ranking Passages for Web Question Answering. The system uses the biLSTM network
with co-attention mechanism between query and passage representations.
Additionally, we use self attention on embeddings to increase the lexical
coverage by allowing the system to take union over different embeddings. We
also incorporate hand-crafted features to improve the system performance. Our
system achieved a Mean Reciprocal Rank (MRR) of 0.67 on eval-1 dataset.
| 2,019 | Computation and Language |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset | Multiple entities in a document generally exhibit complex inter-sentence
relations, and cannot be well handled by existing relation extraction (RE)
methods that typically focus on extracting intra-sentence relations for single
entity pairs. In order to accelerate the research on document-level RE, we
introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with
three features: (1) DocRED annotates both named entities and relations, and is
the largest human-annotated dataset for document-level RE from plain text; (2)
DocRED requires reading multiple sentences in a document to extract entities
and infer their relations by synthesizing all information of the document; (3)
along with the human-annotated data, we also offer large-scale distantly
supervised data, which enables DocRED to be adopted for both supervised and
weakly supervised scenarios. In order to verify the challenges of
document-level RE, we implement recent state-of-the-art methods for RE and
conduct a thorough evaluation of these methods on DocRED. Empirical results
show that DocRED is challenging for existing RE methods, which indicates that
document-level RE remains an open problem and requires further efforts. Based
on the detailed analysis on the experiments, we discuss multiple promising
directions for future research.
| 2,019 | Computation and Language |
NLProlog: Reasoning with Weak Unification for Question Answering in
Natural Language | Rule-based models are attractive for various tasks because they inherently
lead to interpretable and explainable decisions and can easily incorporate
prior knowledge. However, such systems are difficult to apply to problems
involving natural language, due to its linguistic variability. In contrast,
neural models can cope very well with ambiguity by learning distributed
representations of words and their composition from data, but lead to models
that are difficult to interpret. In this paper, we describe a model combining
neural networks with logic programming in a novel manner for solving multi-hop
reasoning tasks over natural language. Specifically, we propose to use a Prolog
prover which we extend to utilize a similarity function over pretrained
sentence encoders. We fine-tune the representations for the similarity function
via backpropagation. This leads to a system that can apply rule-based reasoning
to natural language, and induce domain-specific rules from training data. We
evaluate the proposed system on two different question answering tasks, showing
that it outperforms two baselines -- BIDAF (Seo et al., 2016a) and FAST QA
(Weissenborn et al., 2017b) on a subset of the WikiHop corpus and achieves
competitive results on the MedHop data set (Welbl et al., 2017).
| 2,019 | Computation and Language |
Cumulative Adaptation for BLSTM Acoustic Models | This paper addresses the robust speech recognition problem as an adaptation
task. Specifically, we investigate the cumulative application of adaptation
methods. A bidirectional Long Short-Term Memory (BLSTM) based neural network,
capable of learning temporal relationships and translation invariant
representations, is used for robust acoustic modelling. Further, i-vectors were
used as an input to the neural network to perform instantaneous speaker and
environment adaptation, providing 8\% relative improvement in word error rate
on the NIST Hub5 2000 evaluation test set. By enhancing the first-pass i-vector
based adaptation with a second-pass adaptation using speaker and environment
dependent transformations within the network, a further relative improvement of
5\% in word error rate was achieved. We have reevaluated the features used to
estimate i-vectors and their normalization to achieve the best performance in a
modern large scale automatic speech recognition system.
| 2,019 | Computation and Language |
Improving Visual Question Answering by Referring to Generated Paragraph
Captions | Paragraph-style image captions describe diverse aspects of an image as
opposed to the more common single-sentence captions that only provide an
abstract description of the image. These paragraph captions can hence contain
substantial information of the image for tasks such as visual question
answering. Moreover, this textual information is complementary with visual
information present in the image because it can discuss both more abstract
concepts and more explicit, intermediate symbolic information about objects,
events, and scenes that can directly be matched with the textual question and
copied into the textual answer (i.e., via easier modality match). Hence, we
propose a combined Visual and Textual Question Answering (VTQA) model which
takes as input a paragraph caption as well as the corresponding image, and
answers the given question based on both inputs. In our model, the inputs are
fused to extract related information by cross-attention (early fusion), then
fused again in the form of consensus (late fusion), and finally expected
answers are given an extra score to enhance the chance of selection (later
fusion). Empirical results show that paragraph captions, even when
automatically generated (via an RL-based encoder-decoder model), help correctly
answer more visual questions. Overall, our joint model, when trained on the
Visual Genome dataset, significantly improves the VQA performance over a strong
baseline model.
| 2,019 | Computation and Language |
A Simple and Effective Approach to Automatic Post-Editing with Transfer
Learning | Automatic post-editing (APE) seeks to automatically refine the output of a
black-box machine translation (MT) system through human post-edits. APE systems
are usually trained by complementing human post-edited data with large,
artificial data generated through back-translations, a time-consuming process
often no easier than training an MT system from scratch. In this paper, we
propose an alternative where we fine-tune pre-trained BERT models on both the
encoder and decoder of an APE system, exploring several parameter sharing
strategies. By only training on a dataset of 23K sentences for 3 hours on a
single GPU, we obtain results that are competitive with systems that were
trained on 5M artificial sentences. When we add this artificial data, our
method obtains state-of-the-art results.
| 2,019 | Computation and Language |
IITP at MEDIQA 2019: Systems Report for Natural Language Inference,
Question Entailment and Question Answering | This paper presents the experiments accomplished as a part of our
participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We
participated in all the three tasks defined in this particular shared task. The
tasks are viz. i. Natural Language Inference (NLI) ii. Recognizing Question
Entailment(RQE) and their application in medical Question Answering (QA). We
submitted runs using multiple deep learning based systems (runs) for each of
these three tasks. We submitted five system results in each of the NLI and RQE
tasks, and four system results for the QA task. The systems yield encouraging
results in all three tasks. The highest performance obtained in NLI, RQE and QA
tasks are 81.8%, 53.2%, and 71.7%, respectively.
| 2,021 | Computation and Language |
On the Computational Power of RNNs | Recent neural network architectures such as the basic recurrent neural
network (RNN) and Gated Recurrent Unit (GRU) have gained prominence as
end-to-end learning architectures for natural language processing tasks. But
what is the computational power of such systems? We prove that finite precision
RNNs with one hidden layer and ReLU activation and finite precision GRUs are
exactly as computationally powerful as deterministic finite automata. Allowing
arbitrary precision, we prove that RNNs with one hidden layer and ReLU
activation are at least as computationally powerful as pushdown automata. If we
also allow infinite precision, infinite edge weights, and nonlinear output
activation functions, we prove that GRUs are at least as computationally
powerful as pushdown automata. All results are shown constructively.
| 2,019 | Computation and Language |
Comparison of Diverse Decoding Methods from Conditional Language Models | While conditional language models have greatly improved in their ability to
output high-quality natural language, many NLP applications benefit from being
able to generate a diverse set of candidate sequences. Diverse decoding
strategies aim to, within a given-sized candidate list, cover as much of the
space of high-quality outputs as possible, leading to improvements for tasks
that re-rank and combine candidate outputs. Standard decoding methods, such as
beam search, optimize for generating high likelihood sequences rather than
diverse ones, though recent work has focused on increasing diversity in these
methods. In this work, we perform an extensive survey of decoding-time
strategies for generating diverse outputs from conditional language models. We
also show how diversity can be improved without sacrificing quality by
over-sampling additional candidates, then filtering to the desired number.
| 2,019 | Computation and Language |
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