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What do Deep Networks Like to Read? | Recent research towards understanding neural networks probes models in a
top-down manner, but is only able to identify model tendencies that are known a
priori. We propose Susceptibility Identification through Fine-Tuning (SIFT), a
novel abstractive method that uncovers a model's preferences without imposing
any prior. By fine-tuning an autoencoder with the gradients from a fixed
classifier, we are able to extract propensities that characterize different
kinds of classifiers in a bottom-up manner. We further leverage the SIFT
architecture to rephrase sentences in order to predict the opposing class of
the ground truth label, uncovering potential artifacts encoded in the fixed
classification model. We evaluate our method on three diverse tasks with four
different models. We contrast the propensities of the models as well as
reproduce artifacts reported in the literature.
| 2,019 | Computation and Language |
Human Languages in Source Code: Auto-Translation for Localized
Instruction | Computer science education has promised open access around the world, but
access is largely determined by what human language you speak. As younger
students learn computer science it is less appropriate to assume that they
should learn English beforehand. To that end we present CodeInternational, the
first tool to translate code between human languages. To develop a theory of
non-English code, and inform our translation decisions, we conduct a study of
public code repositories on GitHub. The study is to the best of our knowledge
the first on human-language in code and covers 2.9 million Java repositories.
To demonstrate CodeInternational's educational utility, we build an interactive
version of the popular English-language Karel reader and translate it into 100
spoken languages. Our translations have already been used in classrooms around
the world, and represent a first step in an important open CS-education
problem.
| 2,019 | Computation and Language |
Representation of Constituents in Neural Language Models: Coordination
Phrase as a Case Study | Neural language models have achieved state-of-the-art performances on many
NLP tasks, and recently have been shown to learn a number of
hierarchically-sensitive syntactic dependencies between individual words.
However, equally important for language processing is the ability to combine
words into phrasal constituents, and use constituent-level features to drive
downstream expectations. Here we investigate neural models' ability to
represent constituent-level features, using coordinated noun phrases as a case
study. We assess whether different neural language models trained on English
and French represent phrase-level number and gender features, and use those
features to drive downstream expectations. Our results suggest that models use
a linear combination of NP constituent number to drive CoordNP/verb number
agreement. This behavior is highly regular and even sensitive to local
syntactic context, however it differs crucially from observed human behavior.
Models have less success with gender agreement. Models trained on large corpora
perform best, and there is no obvious advantage for models trained using
explicit syntactic supervision.
| 2,019 | Computation and Language |
Neural Embedding Allocation: Distributed Representations of Topic Models | Word embedding models such as the skip-gram learn vector representations of
words' semantic relationships, and document embedding models learn similar
representations for documents. On the other hand, topic models provide latent
representations of the documents' topical themes. To get the benefits of these
representations simultaneously, we propose a unifying algorithm, called neural
embedding allocation (NEA), which deconstructs topic models into interpretable
vector-space embeddings of words, topics, documents, authors, and so on, by
learning neural embeddings to mimic the topic models. We showcase NEA's
effectiveness and generality on LDA, author-topic models and the recently
proposed mixed membership skip gram topic model and achieve better performance
with the embeddings compared to several state-of-the-art models. Furthermore,
we demonstrate that using NEA to smooth out the topics improves coherence
scores over the original topic models when the number of topics is large.
| 2,019 | Computation and Language |
WIQA: A dataset for "What if..." reasoning over procedural text | We introduce WIQA, the first large-scale dataset of "What if..." questions
over procedural text. WIQA contains three parts: a collection of paragraphs
each describing a process, e.g., beach erosion; a set of crowdsourced influence
graphs for each paragraph, describing how one change affects another; and a
large (40k) collection of "What if...?" multiple-choice questions derived from
the graphs. For example, given a paragraph about beach erosion, would stormy
weather result in more or less erosion (or have no effect)? The task is to
answer the questions, given their associated paragraph. WIQA contains three
kinds of questions: perturbations to steps mentioned in the paragraph; external
(out-of-paragraph) perturbations requiring commonsense knowledge; and
irrelevant (no effect) perturbations. We find that state-of-the-art models
achieve 73.8% accuracy, well below the human performance of 96.3%. We analyze
the challenges, in particular tracking chains of influences, and present the
dataset as an open challenge to the community.
| 2,019 | Computation and Language |
Everything Happens for a Reason: Discovering the Purpose of Actions in
Procedural Text | Our goal is to better comprehend procedural text, e.g., a paragraph about
photosynthesis, by not only predicting what happens, but why some actions need
to happen before others. Our approach builds on a prior process comprehension
framework for predicting actions' effects, to also identify subsequent steps
that those effects enable. We present our new model (XPAD) that biases effect
predictions towards those that (1) explain more of the actions in the paragraph
and (2) are more plausible with respect to background knowledge. We also extend
an existing benchmark dataset for procedural text comprehension, ProPara, by
adding the new task of explaining actions by predicting their dependencies. We
find that XPAD significantly outperforms prior systems on this task, while
maintaining the performance on the original task in ProPara. The dataset is
available at http://data.allenai.org/propara
| 2,019 | Computation and Language |
Scientific Discourse Tagging for Evidence Extraction | Evidence plays a crucial role in any biomedical research narrative, providing
justification for some claims and refutation for others. We seek to build
models of scientific argument using information extraction methods from
full-text papers. We present the capability of automatically extracting text
fragments from primary research papers that describe the evidence presented in
that paper's figures, which arguably provides the raw material of any
scientific argument made within the paper. We apply richly contextualized deep
representation learning pre-trained on biomedical domain corpus to the analysis
of scientific discourse structures and the extraction of "evidence fragments"
(i.e., the text in the results section describing data presented in a specified
subfigure) from a set of biomedical experimental research articles. We first
demonstrate our state-of-the-art scientific discourse tagger on two scientific
discourse tagging datasets and its transferability to new datasets. We then
show the benefit of leveraging scientific discourse tags for downstream tasks
such as claim-extraction and evidence fragment detection. Our work demonstrates
the potential of using evidence fragments derived from figure spans for
improving the quality of scientific claims by cataloging, indexing and reusing
evidence fragments as independent documents.
| 2,021 | Computation and Language |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | Pretrained language models are promising particularly for low-resource
languages as they only require unlabelled data. However, training existing
models requires huge amounts of compute, while pretrained cross-lingual models
often underperform on low-resource languages. We propose Multi-lingual language
model Fine-Tuning (MultiFiT) to enable practitioners to train and fine-tune
language models efficiently in their own language. In addition, we propose a
zero-shot method using an existing pretrained cross-lingual model. We evaluate
our methods on two widely used cross-lingual classification datasets where they
outperform models pretrained on orders of magnitude more data and compute. We
release all models and code.
| 2,020 | Computation and Language |
Definition Frames: Using Definitions for Hybrid Concept Representations | Advances in word representations have shown tremendous improvements in
downstream NLP tasks, but lack semantic interpretability. In this paper, we
introduce Definition Frames (DF), a matrix distributed representation extracted
from definitions, where each dimension is semantically interpretable. DF
dimensions correspond to the Qualia structure relations: a set of relations
that uniquely define a term. Our results show that DFs have competitive
performance with other distributional semantic approaches on word similarity
tasks.
| 2,020 | Computation and Language |
Global Locality in Biomedical Relation and Event Extraction | Due to the exponential growth of biomedical literature, event and relation
extraction are important tasks in biomedical text mining. Most work only focus
on relation extraction, and detect a single entity pair mention on a short span
of text, which is not ideal due to long sentences that appear in biomedical
contexts. We propose an approach to both relation and event extraction, for
simultaneously predicting relationships between all mention pairs in a text. We
also perform an empirical study to discuss different network setups for this
purpose. The best performing model includes a set of multi-head attentions and
convolutions, an adaptation of the transformer architecture, which offers
self-attention the ability to strengthen dependencies among related elements,
and models the interaction between features extracted by multiple attention
heads. Experiment results demonstrate that our approach outperforms the state
of the art on a set of benchmark biomedical corpora including BioNLP 2009,
2011, 2013 and BioCreative 2017 shared tasks.
| 2,020 | Computation and Language |
A Discrete Hard EM Approach for Weakly Supervised Question Answering | Many question answering (QA) tasks only provide weak supervision for how the
answer should be computed. For example, TriviaQA answers are entities that can
be mentioned multiple times in supporting documents, while DROP answers can be
computed by deriving many different equations from numbers in the reference
text. In this paper, we show it is possible to convert such tasks into discrete
latent variable learning problems with a precomputed, task-specific set of
possible "solutions" (e.g. different mentions or equations) that contains one
correct option. We then develop a hard EM learning scheme that computes
gradients relative to the most likely solution at each update. Despite its
simplicity, we show that this approach significantly outperforms previous
methods on six QA tasks, including absolute gains of 2--10%, and achieves the
state-of-the-art on five of them. Using hard updates instead of maximizing
marginal likelihood is key to these results as it encourages the model to find
the one correct answer, which we show through detailed qualitative analysis.
| 2,019 | Computation and Language |
Dynamic Fusion: Attentional Language Model for Neural Machine
Translation | Neural Machine Translation (NMT) can be used to generate fluent output. As
such, language models have been investigated for incorporation with NMT. In
prior investigations, two models have been used: a translation model and a
language model. The translation model's predictions are weighted by the
language model with a hand-crafted ratio in advance. However, these approaches
fail to adopt the language model weighting with regard to the translation
history. In another line of approach, language model prediction is incorporated
into the translation model by jointly considering source and target
information. However, this line of approach is limited because it largely
ignores the adequacy of the translation output.
Accordingly, this work employs two mechanisms, the translation model and the
language model, with an attentive architecture to the language model as an
auxiliary element of the translation model. Compared with previous work in
English--Japanese machine translation using a language model, the experimental
results obtained with the proposed Dynamic Fusion mechanism improve BLEU and
Rank-based Intuitive Bilingual Evaluation Scores (RIBES) scores. Additionally,
in the analyses of the attention and predictivity of the language model, the
Dynamic Fusion mechanism allows predictive language modeling that conforms to
the appropriate grammatical structure.
| 2,019 | Computation and Language |
Comprehensive Analysis of Aspect Term Extraction Methods using Various
Text Embeddings | Recently, a variety of model designs and methods have blossomed in the
context of the sentiment analysis domain. However, there is still a lack of
wide and comprehensive studies of aspect-based sentiment analysis (ABSA). We
want to fill this gap and propose a comparison with ablation analysis of aspect
term extraction using various text embedding methods. We particularly focused
on architectures based on long short-term memory (LSTM) with optional
conditional random field (CRF) enhancement using different pre-trained word
embeddings. Moreover, we analyzed the influence on the performance of extending
the word vectorization step with character embedding. The experimental results
on SemEval datasets revealed that not only does bi-directional long short-term
memory (BiLSTM) outperform regular LSTM, but also word embedding coverage and
its source highly affect aspect detection performance. An additional CRF layer
consistently improves the results as well.
| 2,020 | Computation and Language |
How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer
Representations | Bidirectional Encoder Representations from Transformers (BERT) reach
state-of-the-art results in a variety of Natural Language Processing tasks.
However, understanding of their internal functioning is still insufficient and
unsatisfactory. In order to better understand BERT and other Transformer-based
models, we present a layer-wise analysis of BERT's hidden states. Unlike
previous research, which mainly focuses on explaining Transformer models by
their attention weights, we argue that hidden states contain equally valuable
information. Specifically, our analysis focuses on models fine-tuned on the
task of Question Answering (QA) as an example of a complex downstream task. We
inspect how QA models transform token vectors in order to find the correct
answer. To this end, we apply a set of general and QA-specific probing tasks
that reveal the information stored in each representation layer. Our
qualitative analysis of hidden state visualizations provides additional
insights into BERT's reasoning process. Our results show that the
transformations within BERT go through phases that are related to traditional
pipeline tasks. The system can therefore implicitly incorporate task-specific
information into its token representations. Furthermore, our analysis reveals
that fine-tuning has little impact on the models' semantic abilities and that
prediction errors can be recognized in the vector representations of even early
layers.
| 2,019 | Computation and Language |
BERTgrid: Contextualized Embedding for 2D Document Representation and
Understanding | For understanding generic documents, information like font sizes, column
layout, and generally the positioning of words may carry semantic information
that is crucial for solving a downstream document intelligence task. Our novel
BERTgrid, which is based on Chargrid by Katti et al. (2018), represents a
document as a grid of contextualized word piece embedding vectors, thereby
making its spatial structure and semantics accessible to the processing neural
network. The contextualized embedding vectors are retrieved from a BERT
language model. We use BERTgrid in combination with a fully convolutional
network on a semantic instance segmentation task for extracting fields from
invoices. We demonstrate its performance on tabulated line item and document
header field extraction.
| 2,019 | Computation and Language |
Learning Dynamic Author Representations with Temporal Language Models | Language models are at the heart of numerous works, notably in the text
mining and information retrieval communities. These statistical models aim at
extracting word distributions, from simple unigram models to recurrent
approaches with latent variables that capture subtle dependencies in texts.
However, those models are learned from word sequences only, and authors'
identities, as well as publication dates, are seldom considered. We propose a
neural model, based on recurrent language modeling, which aims at capturing
language diffusion tendencies in author communities through time. By
conditioning language models with author and temporal vector states, we are
able to leverage the latent dependencies between the text contexts. This allows
us to beat several temporal and non-temporal language baselines on two
real-world corpora, and to learn meaningful author representations that vary
through time.
| 2,020 | Computation and Language |
Proposal Towards a Personalized Knowledge-powered Self-play Based
Ensemble Dialog System | This is the application document for the 2019 Amazon Alexa competition. We
give an overall vision of our conversational experience, as well as a sample
conversation that we would like our dialog system to achieve by the end of the
competition. We believe personalization, knowledge, and self-play are important
components towards better chatbots. These are further highlighted by our
detailed system architecture proposal and novelty section. Finally, we describe
how we would ensure an engaging experience, how this research would impact the
field, and related work.
| 2,019 | Computation and Language |
Question Generation by Transformers | A machine learning model was developed to automatically generate questions
from Wikipedia passages using transformers, an attention-based model eschewing
the paradigm of existing recurrent neural networks (RNNs). The model was
trained on the inverted Stanford Question Answering Dataset (SQuAD), which is a
reading comprehension dataset consisting of 100,000+ questions posed by
crowdworkers on a set of Wikipedia articles. After training, the question
generation model is able to generate simple questions relevant to unseen
passages and answers containing an average of 8 words per question. The word
error rate (WER) was used as a metric to compare the similarity between SQuAD
questions and the model-generated questions. Although the high average WER
suggests that the questions generated differ from the original SQuAD questions,
the questions generated are mostly grammatically correct and plausible in their
own right.
| 2,019 | Computation and Language |
Getting Gender Right in Neural Machine Translation | Speakers of different languages must attend to and encode strikingly
different aspects of the world in order to use their language correctly (Sapir,
1921; Slobin, 1996). One such difference is related to the way gender is
expressed in a language. Saying "I am happy" in English, does not encode any
additional knowledge of the speaker that uttered the sentence. However, many
other languages do have grammatical gender systems and so such knowledge would
be encoded. In order to correctly translate such a sentence into, say, French,
the inherent gender information needs to be retained/recovered. The same
sentence would become either "Je suis heureux", for a male speaker or "Je suis
heureuse" for a female one. Apart from morphological agreement, demographic
factors (gender, age, etc.) also influence our use of language in terms of word
choices or even on the level of syntactic constructions (Tannen, 1991;
Pennebaker et al., 2003). We integrate gender information into NMT systems. Our
contribution is two-fold: (1) the compilation of large datasets with speaker
information for 20 language pairs, and (2) a simple set of experiments that
incorporate gender information into NMT for multiple language pairs. Our
experiments show that adding a gender feature to an NMT system significantly
improves the translation quality for some language pairs.
| 2,018 | Computation and Language |
From English to Code-Switching: Transfer Learning with Strong
Morphological Clues | Linguistic Code-switching (CS) is still an understudied phenomenon in natural
language processing. The NLP community has mostly focused on monolingual and
multi-lingual scenarios, but little attention has been given to CS in
particular. This is partly because of the lack of resources and annotated data,
despite its increasing occurrence in social media platforms. In this paper, we
aim at adapting monolingual models to code-switched text in various tasks.
Specifically, we transfer English knowledge from a pre-trained ELMo model to
different code-switched language pairs (i.e., Nepali-English, Spanish-English,
and Hindi-English) using the task of language identification. Our method,
CS-ELMo, is an extension of ELMo with a simple yet effective position-aware
attention mechanism inside its character convolutions. We show the
effectiveness of this transfer learning step by outperforming multilingual BERT
and homologous CS-unaware ELMo models and establishing a new state of the art
in CS tasks, such as NER and POS tagging. Our technique can be expanded to more
English-paired code-switched languages, providing more resources to the CS
community.
| 2,020 | Computation and Language |
Dependency-Aware Named Entity Recognition with Relative and Global
Attentions | Named entity recognition is one of the core tasks in NLP. Although many
improvements have been made on this task during the last years, the
state-of-the-art systems do not explicitly take into account the recursive
nature of language. Instead of only treating the text as a plain sequence of
words, we incorporate a linguistically-inspired way to recognize entities based
on syntax and tree structures. Our model exploits syntactic relationships among
words using a Tree-LSTM guided by dependency trees. Then, we enhance these
features by applying relative and global attention mechanisms. On the one hand,
the relative attention detects the most informative words in the sentence with
respect to the word being evaluated. On the other hand, the global attention
spots the most relevant words in the sequence. Lastly, we linearly project the
weighted vectors into the tagging space so that a conditional random field
classifier predicts the entity labels. Our findings show that the model detects
words that disclose the entity types based on their syntactic roles in a
sentence (e.g., verbs such as speak and write are attended when the entity type
is PERSON, whereas meet and travel strongly relate to LOCATION). We confirm our
findings and establish a new state of the art on two datasets.
| 2,019 | Computation and Language |
The Longer the Better? The Interplay Between Review Length and Line of
Argumentation in Online Consumer Reviews | Review helpfulness serves as focal point in understanding customers' purchase
decision-making process on online retailer platforms. An overwhelming majority
of previous works find longer reviews to be more helpful than short reviews. In
this paper, we propose that longer reviews should not be assumed to be
uniformly more helpful; instead, we argue that the effect depends on the line
of argumentation in the review text. To test this idea, we use a large dataset
of customer reviews from Amazon in combination with a state-of-the-art approach
from natural language processing that allows us to study argumentation lines at
sentence level. Our empirical analysis suggests that the frequency of
argumentation changes moderates the effect of review length on helpfulness.
Altogether, we disprove the prevailing narrative that longer reviews are
uniformly perceived as more helpful. Our findings allow retailer platforms to
improve their customer feedback systems and to feature more useful product
reviews.
| 2,019 | Computation and Language |
Self-Attentional Models Application in Task-Oriented Dialogue Generation
Systems | Self-attentional models are a new paradigm for sequence modelling tasks which
differ from common sequence modelling methods, such as recurrence-based and
convolution-based sequence learning, in the way that their architecture is only
based on the attention mechanism. Self-attentional models have been used in the
creation of the state-of-the-art models in many NLP tasks such as neural
machine translation, but their usage has not been explored for the task of
training end-to-end task-oriented dialogue generation systems yet. In this
study, we apply these models on the three different datasets for training
task-oriented chatbots. Our finding shows that self-attentional models can be
exploited to create end-to-end task-oriented chatbots which not only achieve
higher evaluation scores compared to recurrence-based models, but also do so
more efficiently.
| 2,019 | Computation and Language |
Frustratingly Easy Natural Question Answering | Existing literature on Question Answering (QA) mostly focuses on algorithmic
novelty, data augmentation, or increasingly large pre-trained language models
like XLNet and RoBERTa. Additionally, a lot of systems on the QA leaderboards
do not have associated research documentation in order to successfully
replicate their experiments. In this paper, we outline these algorithmic
components such as Attention-over-Attention, coupled with data augmentation and
ensembling strategies that have shown to yield state-of-the-art results on
benchmark datasets like SQuAD, even achieving super-human performance. Contrary
to these prior results, when we evaluate on the recently proposed Natural
Questions benchmark dataset, we find that an incredibly simple approach of
transfer learning from BERT outperforms the previous state-of-the-art system
trained on 4 million more examples than ours by 1.9 F1 points. Adding
ensembling strategies further improves that number by 2.3 F1 points.
| 2,019 | Computation and Language |
Identifying Editor Roles in Argumentative Writing from Student Revision
Histories | We present a method for identifying editor roles from students' revision
behaviors during argumentative writing. We first develop a method for applying
a topic modeling algorithm to identify a set of editor roles from a vocabulary
capturing three aspects of student revision behaviors: operation, purpose, and
position. We validate the identified roles by showing that modeling the editor
roles that students take when revising a paper not only accounts for the
variance in revision purposes in our data, but also relates to writing
improvement.
| 2,019 | Computation and Language |
Annotation and Classification of Sentence-level Revision Improvement | Studies of writing revisions rarely focus on revision quality. To address
this issue, we introduce a corpus of between-draft revisions of student
argumentative essays, annotated as to whether each revision improves essay
quality. We demonstrate a potential usage of our annotations by developing a
machine learning model to predict revision improvement. With the goal of
expanding training data, we also extract revisions from a dataset edited by
expert proofreaders. Our results indicate that blending expert and non-expert
revisions increases model performance, with expert data particularly important
for predicting low-quality revisions.
| 2,018 | Computation and Language |
Graph-Based Reasoning over Heterogeneous External Knowledge for
Commonsense Question Answering | Commonsense question answering aims to answer questions which require
background knowledge that is not explicitly expressed in the question. The key
challenge is how to obtain evidence from external knowledge and make
predictions based on the evidence. Recent works either learn to generate
evidence from human-annotated evidence which is expensive to collect, or
extract evidence from either structured or unstructured knowledge bases which
fails to take advantages of both sources. In this work, we propose to
automatically extract evidence from heterogeneous knowledge sources, and answer
questions based on the extracted evidence. Specifically, we extract evidence
from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain
texts. We construct graphs for both sources to obtain the relational structures
of evidence. Based on these graphs, we propose a graph-based approach
consisting of a graph-based contextual word representation learning module and
a graph-based inference module. The first module utilizes graph structural
information to re-define the distance between words for learning better
contextual word representations. The second module adopts graph convolutional
network to encode neighbor information into the representations of nodes, and
aggregates evidence with graph attention mechanism for predicting the final
answer. Experimental results on CommonsenseQA dataset illustrate that our
graph-based approach over both knowledge sources brings improvement over strong
baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the
CommonsenseQA leaderboard.
| 2,020 | Computation and Language |
What Makes A Good Story? Designing Composite Rewards for Visual
Storytelling | Previous storytelling approaches mostly focused on optimizing traditional
metrics such as BLEU, ROUGE and CIDEr. In this paper, we re-examine this
problem from a different angle, by looking deep into what defines a
realistically-natural and topically-coherent story. To this end, we propose
three assessment criteria: relevance, coherence and expressiveness, which we
observe through empirical analysis could constitute a "high-quality" story to
the human eye. Following this quality guideline, we propose a reinforcement
learning framework, ReCo-RL, with reward functions designed to capture the
essence of these quality criteria. Experiments on the Visual Storytelling
Dataset (VIST) with both automatic and human evaluations demonstrate that our
ReCo-RL model achieves better performance than state-of-the-art baselines on
both traditional metrics and the proposed new criteria.
| 2,020 | Computation and Language |
Let's Ask Again: Refine Network for Automatic Question Generation | In this work, we focus on the task of Automatic Question Generation (AQG)
where given a passage and an answer the task is to generate the corresponding
question. It is desired that the generated question should be (i) grammatically
correct (ii) answerable from the passage and (iii) specific to the given
answer. An analysis of existing AQG models shows that they produce questions
which do not adhere to one or more of {the above-mentioned qualities}. In
particular, the generated questions look like an incomplete draft of the
desired question with a clear scope for refinement. {To alleviate this
shortcoming}, we propose a method which tries to mimic the human process of
generating questions by first creating an initial draft and then refining it.
More specifically, we propose Refine Network (RefNet) which contains two
decoders. The second decoder uses a dual attention network which pays attention
to both (i) the original passage and (ii) the question (initial draft)
generated by the first decoder. In effect, it refines the question generated by
the first decoder, thereby making it more correct and complete. We evaluate
RefNet on three datasets, \textit{viz.}, SQuAD, HOTPOT-QA, and DROP, and show
that it outperforms existing state-of-the-art methods by 7-16\% on all of these
datasets. Lastly, we show that we can improve the quality of the second decoder
on specific metrics, such as, fluency and answerability by explicitly rewarding
revisions that improve on the corresponding metric during training. The code
has been made publicly available
\footnote{https://github.com/PrekshaNema25/RefNet-QG}
| 2,019 | Computation and Language |
Entity Projection via Machine Translation for Cross-Lingual NER | Although over 100 languages are supported by strong off-the-shelf machine
translation systems, only a subset of them possess large annotated corpora for
named entity recognition. Motivated by this fact, we leverage machine
translation to improve annotation-projection approaches to cross-lingual named
entity recognition. We propose a system that improves over prior
entity-projection methods by: (a) leveraging machine translation systems twice:
first for translating sentences and subsequently for translating entities; (b)
matching entities based on orthographic and phonetic similarity; and (c)
identifying matches based on distributional statistics derived from the
dataset. Our approach improves upon current state-of-the-art methods for
cross-lingual named entity recognition on 5 diverse languages by an average of
4.1 points. Further, our method achieves state-of-the-art F_1 scores for
Armenian, outperforming even a monolingual model trained on Armenian source
data.
| 2,019 | Computation and Language |
Out-of-Domain Detection for Low-Resource Text Classification Tasks | Out-of-domain (OOD) detection for low-resource text classification is a
realistic but understudied task. The goal is to detect the OOD cases with
limited in-domain (ID) training data, since we observe that training data is
often insufficient in machine learning applications. In this work, we propose
an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection
and few-shot ID classification task. Evaluation on real-world datasets show
that the proposed solution outperforms state-of-the-art methods in zero-shot
OOD detection task, while maintaining a competitive performance on ID
classification task.
| 2,019 | Computation and Language |
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset | A significant barrier to progress in data-driven approaches to building
dialog systems is the lack of high quality, goal-oriented conversational data.
To help satisfy this elementary requirement, we introduce the initial release
of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising
six domains. Two procedures were used to create this collection, each with
unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz)
approach in which trained agents and crowdsourced workers interact to complete
the task while the second is "self-dialog" in which crowdsourced workers write
the entire dialog themselves. We do not restrict the workers to detailed
scripts or to a small knowledge base and hence we observe that our dataset
contains more realistic and diverse conversations in comparison to existing
datasets. We offer several baseline models including state of the art neural
seq2seq architectures with benchmark performance as well as qualitative human
evaluations. Dialogs are labeled with API calls and arguments, a simple and
cost effective approach which avoids the requirement of complex annotation
schema. The layer of abstraction between the dialog model and the service
provider API allows for a given model to interact with multiple services that
provide similar functionally. Finally, the dataset will evoke interest in
written vs. spoken language, discourse patterns, error handling and other
linguistic phenomena related to dialog system research, development and design.
| 2,019 | Computation and Language |
From Textual Information Sources to Linked Data in the Agatha Project | Automatic reasoning about textual information is a challenging task in modern
Natural Language Processing (NLP) systems. In this work we describe our
proposal for representing and reasoning about Portuguese documents by means of
Linked Data like ontologies and thesauri. Our approach resorts to a specialized
pipeline of natural language processing (part-of-speech tagger, named entity
recognition, semantic role labeling) to populate an ontology for the domain of
criminal investigations. The provided architecture and ontology are language
independent. Although some of the NLP modules are language dependent, they can
be built using adequate AI methodologies.
| 2,019 | Computation and Language |
Joint Event and Temporal Relation Extraction with Shared Representations
and Structured Prediction | We propose a joint event and temporal relation extraction model with shared
representation learning and structured prediction. The proposed method has two
advantages over existing work. First, it improves event representation by
allowing the event and relation modules to share the same contextualized
embeddings and neural representation learner. Second, it avoids error
propagation in the conventional pipeline systems by leveraging structured
inference and learning methods to assign both the event labels and the temporal
relation labels jointly. Experiments show that the proposed method can improve
both event extraction and temporal relation extraction over state-of-the-art
systems, with the end-to-end F1 improved by 10% and 6.8% on two benchmark
datasets respectively.
| 2,020 | Computation and Language |
Structuring Latent Spaces for Stylized Response Generation | Generating responses in a targeted style is a useful yet challenging task,
especially in the absence of parallel data. With limited data, existing methods
tend to generate responses that are either less stylized or less
context-relevant. We propose StyleFusion, which bridges conversation modeling
and non-parallel style transfer by sharing a structured latent space. This
structure allows the system to generate stylized relevant responses by sampling
in the neighborhood of the conversation model prediction, and continuously
control the style level. We demonstrate this method using dialogues from Reddit
data and two sets of sentences with distinct styles (arXiv and Sherlock Holmes
novels). Automatic and human evaluation show that, without sacrificing
appropriateness, the system generates responses of the targeted style and
outperforms competitive baselines.
| 2,019 | Computation and Language |
Problems with automating translation of movie/TV show subtitles | We present 27 problems encountered in automating the translation of movie/TV
show subtitles. We categorize each problem in one of the three categories viz.
problems directly related to textual translation, problems related to subtitle
creation guidelines, and problems due to adaptability of machine translation
(MT) engines. We also present the findings of a translation quality evaluation
experiment where we share the frequency of 16 key problems. We show that the
systems working at the frontiers of Natural Language Processing do not perform
well for subtitles and require some post-processing solutions for redressal of
these problems
| 2,019 | Computation and Language |
Identifying and Explaining Discriminative Attributes | Identifying what is at the center of the meaning of a word and what
discriminates it from other words is a fundamental natural language inference
task. This paper describes an explicit word vector representation model (WVM)
to support the identification of discriminative attributes. A core contribution
of the paper is a quantitative and qualitative comparative analysis of
different types of data sources and Knowledge Bases in the construction of
explainable and explicit WVMs: (i) knowledge graphs built from dictionary
definitions, (ii) entity-attribute-relationships graphs derived from images and
(iii) commonsense knowledge graphs. Using a detailed quantitative and
qualitative analysis, we demonstrate that these data sources have complementary
semantic aspects, supporting the creation of explicit semantic vector spaces.
The explicit vector spaces are evaluated using the task of discriminative
attribute identification, showing comparable performance to the
state-of-the-art systems in the task (F1-score = 0.69), while delivering full
model transparency and explainability.
| 2,019 | Computation and Language |
TransSent: Towards Generation of Structured Sentences with Discourse
Marker | Structured sentences are important expressions in human writings and
dialogues. Previous works on neural text generation fused semantic and
structural information by encoding the entire sentence into a mixed hidden
representation. However, when a generated sentence becomes complicated, the
structure is difficult to be properly maintained. To alleviate this problem, we
explicitly separate the modeling process of semantic and structural
information. Intuitively, humans generate structured sentences by directly
connecting discourses with discourse markers (such as and, but, etc.).
Therefore, we propose a task that mimics this process, called discourse
transfer. This task represents a structured sentence as (head discourse,
discourse marker, tail discourse), and aims at tail discourse generation based
on head discourse and discourse marker. We also propose a corresponding model
called TransSent, which interprets the relationship between two discourses as a
translation1 from the head discourse to the tail discourse in the embedding
space. We experiment TransSent not only in discourse transfer task but also in
free text generation and dialogue generation tasks. Automatic and human
evaluation results show that TransSent can generate structured sentences with
high quality, and has certain scalability in different tasks.
| 2,020 | Computation and Language |
Building Task-Oriented Visual Dialog Systems Through Alternative
Optimization Between Dialog Policy and Language Generation | Reinforcement learning (RL) is an effective approach to learn an optimal
dialog policy for task-oriented visual dialog systems. A common practice is to
apply RL on a neural sequence-to-sequence (seq2seq) framework with the action
space being the output vocabulary in the decoder. However, it is difficult to
design a reward function that can achieve a balance between learning an
effective policy and generating a natural dialog response. This paper proposes
a novel framework that alternatively trains a RL policy for image guessing and
a supervised seq2seq model to improve dialog generation quality. We evaluate
our framework on the GuessWhich task and the framework achieves the
state-of-the-art performance in both task completion and dialog quality.
| 2,019 | Computation and Language |
Learning in Text Streams: Discovery and Disambiguation of Entity and
Relation Instances | We consider a scenario where an artificial agent is reading a stream of text
composed of a set of narrations, and it is informed about the identity of some
of the individuals that are mentioned in the text portion that is currently
being read. The agent is expected to learn to follow the narrations, thus
disambiguating mentions and discovering new individuals. We focus on the case
in which individuals are entities and relations, and we propose an end-to-end
trainable memory network that learns to discover and disambiguate them in an
online manner, performing one-shot learning, and dealing with a small number of
sparse supervisions. Our system builds a not-given-in-advance knowledge base,
and it improves its skills while reading unsupervised text. The model deals
with abrupt changes in the narration, taking into account their effects when
resolving co-references. We showcase the strong disambiguation and discovery
skills of our model on a corpus of Wikipedia documents and on a newly
introduced dataset, that we make publicly available.
| 2,020 | Computation and Language |
An Auxiliary Classifier Generative Adversarial Framework for Relation
Extraction | Relation extraction models suffer from limited qualified training data. Using
human annotators to label sentences is too expensive and does not scale well
especially when dealing with large datasets. In this paper, we use Auxiliary
Classifier Generative Adversarial Networks (AC-GANs) to generate high-quality
relational sentences and to improve the performance of relation classifier in
end-to-end models. In AC-GAN, the discriminator gives not only a probability
distribution over the real source, but also a probability distribution over the
relation labels. This helps to generate meaningful relational sentences.
Experimental results show that our proposed data augmentation method
significantly improves the performance of relation extraction compared to
state-of-the-art methods
| 2,019 | Computation and Language |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain
Natural Language Interfaces to Databases | We present CoSQL, a corpus for building cross-domain, general-purpose
database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+
annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k
dialogues querying 200 complex DBs spanning 138 domains. Each dialogue
simulates a real-world DB query scenario with a crowd worker as a user
exploring the DB and a SQL expert retrieving answers with SQL, clarifying
ambiguous questions, or otherwise informing of unanswerable questions. When
user questions are answerable by SQL, the expert describes the SQL and
execution results to the user, hence maintaining a natural interaction flow.
CoSQL introduces new challenges compared to existing task-oriented dialogue
datasets:(1) the dialogue states are grounded in SQL, a domain-independent
executable representation, instead of domain-specific slot-value pairs, and (2)
because testing is done on unseen databases, success requires generalizing to
new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking,
response generation from query results, and user dialogue act prediction. We
evaluate a set of strong baselines for each task and show that CoSQL presents
significant challenges for future research. The dataset, baselines, and
leaderboard will be released at https://yale-lily.github.io/cosql.
| 2,019 | Computation and Language |
Speculative Beam Search for Simultaneous Translation | Beam search is universally used in full-sentence translation but its
application to simultaneous translation remains non-trivial, where output words
are committed on the fly. In particular, the recently proposed wait-k policy
(Ma et al., 2019a) is a simple and effective method that (after an initial
wait) commits one output word on receiving each input word, making beam search
seemingly impossible. To address this challenge, we propose a speculative beam
search algorithm that hallucinates several steps into the future in order to
reach a more accurate decision, implicitly benefiting from a target language
model. This makes beam search applicable for the first time to the generation
of a single word in each step. Experiments over diverse language pairs show
large improvements over previous work.
| 2,019 | Computation and Language |
VizSeq: A Visual Analysis Toolkit for Text Generation Tasks | Automatic evaluation of text generation tasks (e.g. machine translation, text
summarization, image captioning and video description) usually relies heavily
on task-specific metrics, such as BLEU and ROUGE. They, however, are abstract
numbers and are not perfectly aligned with human assessment. This suggests
inspecting detailed examples as a complement to identify system error patterns.
In this paper, we present VizSeq, a visual analysis toolkit for instance-level
and corpus-level system evaluation on a wide variety of text generation tasks.
It supports multimodal sources and multiple text references, providing
visualization in Jupyter notebook or a web app interface. It can be used
locally or deployed onto public servers for centralized data hosting and
benchmarking. It covers most common n-gram based metrics accelerated with
multiprocessing, and also provides latest embedding-based metrics such as
BERTScore.
| 2,019 | Computation and Language |
Neural Semantic Parsing in Low-Resource Settings with Back-Translation
and Meta-Learning | Neural semantic parsing has achieved impressive results in recent years, yet
its success relies on the availability of large amounts of supervised data. Our
goal is to learn a neural semantic parser when only prior knowledge about a
limited number of simple rules is available, without access to either annotated
programs or execution results. Our approach is initialized by rules, and
improved in a back-translation paradigm using generated question-program pairs
from the semantic parser and the question generator. A phrase table with
frequent mapping patterns is automatically derived, also updated as training
progresses, to measure the quality of generated instances. We train the model
with model-agnostic meta-learning to guarantee the accuracy and stability on
examples covered by rules, and meanwhile acquire the versatility to generalize
well on examples uncovered by rules. Results on three benchmark datasets with
different domains and programs show that our approach incrementally improves
the accuracy. On WikiSQL, our best model is comparable to the SOTA system
learned from denotations.
| 2,019 | Computation and Language |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural
Expectation-Maximization Framework | Distant supervision for relation extraction enables one to effectively
acquire structured relations out of very large text corpora with less human
efforts. Nevertheless, most of the prior-art models for such tasks assume that
the given text can be noisy, but their corresponding labels are clean. Such
unrealistic assumption is contradictory with the fact that the given labels are
often noisy as well, thus leading to significant performance degradation of
those models on real-world data. To cope with this challenge, we propose a
novel label-denoising framework that combines neural network with probabilistic
modelling, which naturally takes into account the noisy labels during learning.
We empirically demonstrate that our approach significantly improves the current
art in uncovering the ground-truth relation labels.
| 2,019 | Computation and Language |
Visualizing Trends of Key Roles in News Articles | There are tons of news articles generated every day reflecting the activities
of key roles such as people, organizations and political parties. Analyzing
these key roles allows us to understand the trends in news. In this paper, we
present a demonstration system that visualizes the trend of key roles in news
articles based on natural language processing techniques. Specifically, we
apply a semantic role labeler and the dynamic word embedding technique to
understand relationships between key roles in the news across different time
periods and visualize the trends of key role and news topics change over time.
| 2,019 | Computation and Language |
A Robust Hybrid Approach for Textual Document Classification | Text document classification is an important task for diverse natural
language processing based applications. Traditional machine learning approaches
mainly focused on reducing dimensionality of textual data to perform
classification. This although improved the overall classification accuracy, the
classifiers still faced sparsity problem due to lack of better data
representation techniques. Deep learning based text document classification, on
the other hand, benefitted greatly from the invention of word embeddings that
have solved the sparsity problem and researchers focus mainly remained on the
development of deep architectures. Deeper architectures, however, learn some
redundant features that limit the performance of deep learning based solutions.
In this paper, we propose a two stage text document classification methodology
which combines traditional feature engineering with automatic feature
engineering (using deep learning). The proposed methodology comprises a filter
based feature selection (FSE) algorithm followed by a deep convolutional neural
network. This methodology is evaluated on the two most commonly used public
datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal
that the proposed methodology outperforms the state-of-the-art of both the
(traditional) machine learning and deep learning based text document
classification methodologies with a significant margin of 7.7% on 20 Newsgroups
and 6.6% on BBC news datasets.
| 2,019 | Computation and Language |
Classifying Multilingual User Feedback using Traditional Machine
Learning and Deep Learning | With the rise of social media like Twitter and of software distribution
platforms like app stores, users got various ways to express their opinion
about software products. Popular software vendors get user feedback
thousandfold per day. Research has shown that such feedback contains valuable
information for software development teams such as problem reports or feature
and support inquires. Since the manual analysis of user feedback is cumbersome
and hard to manage many researchers and tool vendors suggested to use automated
analyses based on traditional supervised machine learning approaches. In this
work, we compare the results of traditional machine learning and deep learning
in classifying user feedback in English and Italian into problem reports,
inquiries, and irrelevant. Our results show that using traditional machine
learning, we can still achieve comparable results to deep learning, although we
collected thousands of labels.
| 2,019 | Computation and Language |
CUNI System for the Building Educational Applications 2019 Shared Task:
Grammatical Error Correction | In this paper, we describe our systems submitted to the Building Educational
Applications (BEA) 2019 Shared Task (Bryant et al., 2019). We participated in
all three tracks. Our models are NMT systems based on the Transformer model,
which we improve by incorporating several enhancements: applying dropout to
whole source and target words, weighting target subwords, averaging model
checkpoints, and using the trained model iteratively for correcting the
intermediate translations. The system in the Restricted Track is trained on the
provided corpora with oversampled "cleaner" sentences and reaches 59.39 F0.5
score on the test set. The system in the Low-Resource Track is trained from
Wikipedia revision histories and reaches 44.13 F0.5 score. Finally, we finetune
the system from the Low-Resource Track on restricted data and achieve 64.55
F0.5 score, placing third in the Unrestricted Track.
| 2,019 | Computation and Language |
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction
System | We present ABSApp, a portable system for weakly-supervised aspect-based
sentiment extraction. The system is interpretable and user friendly and does
not require labeled training data, hence can be rapidly and cost-effectively
used across different domains in applied setups. The system flow includes three
stages: First, it generates domain-specific aspect and opinion lexicons based
on an unlabeled dataset; second, it enables the user to view and edit those
lexicons (weak supervision); and finally, it enables the user to select an
unlabeled target dataset from the same domain, classify it, and generate an
aspect-based sentiment report. ABSApp has been successfully used in a number of
real-life use cases, among them movie review analysis and convention impact
analysis.
| 2,019 | Computation and Language |
Learning Alignment for Multimodal Emotion Recognition from Speech | Speech emotion recognition is a challenging problem because human convey
emotions in subtle and complex ways. For emotion recognition on human speech,
one can either extract emotion related features from audio signals or employ
speech recognition techniques to generate text from speech and then apply
natural language processing to analyze the sentiment. Further, emotion
recognition will be beneficial from using audio-textual multimodal information,
it is not trivial to build a system to learn from multimodality. One can build
models for two input sources separately and combine them in a decision level,
but this method ignores the interaction between speech and text in the temporal
domain. In this paper, we propose to use an attention mechanism to learn the
alignment between speech frames and text words, aiming to produce more accurate
multimodal feature representations. The aligned multimodal features are fed
into a sequential model for emotion recognition. We evaluate the approach on
the IEMOCAP dataset and the experimental results show the proposed approach
achieves the state-of-the-art performance on the dataset.
| 2,020 | Computation and Language |
UER: An Open-Source Toolkit for Pre-training Models | Existing works, including ELMO and BERT, have revealed the importance of
pre-training for NLP tasks. While there does not exist a single pre-training
model that works best in all cases, it is of necessity to develop a framework
that is able to deploy various pre-training models efficiently. For this
purpose, we propose an assemble-on-demand pre-training toolkit, namely
Universal Encoder Representations (UER). UER is loosely coupled, and
encapsulated with rich modules. By assembling modules on demand, users can
either reproduce a state-of-the-art pre-training model or develop a
pre-training model that remains unexplored. With UER, we have built a model
zoo, which contains pre-trained models based on different corpora, encoders,
and targets (objectives). With proper pre-trained models, we could achieve new
state-of-the-art results on a range of downstream datasets.
| 2,019 | Computation and Language |
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary
Induction | The task of bilingual dictionary induction (BDI) is commonly used for
intrinsic evaluation of cross-lingual word embeddings. The largest dataset for
BDI was generated automatically, so its quality is dubious. We study the
composition and quality of the test sets for five diverse languages from this
dataset, with concerning findings: (1) a quarter of the data consists of proper
nouns, which can be hardly indicative of BDI performance, and (2) there are
pervasive gaps in the gold-standard targets. These issues appear to affect the
ranking between cross-lingual embedding systems on individual languages, and
the overall degree to which the systems differ in performance. With proper
nouns removed from the data, the margin between the top two systems included in
the study grows from 3.4% to 17.2%. Manual verification of the predictions, on
the other hand, reveals that gaps in the gold standard targets artificially
inflate the margin between the two systems on English to Bulgarian BDI from
0.1% to 6.7%. We thus suggest that future research either avoids drawing
conclusions from quantitative results on this BDI dataset, or accompanies such
evaluation with rigorous error analysis.
| 2,019 | Computation and Language |
Fine-Grained Entity Typing for Domain Independent Entity Linking | Neural entity linking models are very powerful, but run the risk of
overfitting to the domain they are trained in. For this problem, a domain is
characterized not just by genre of text but even by factors as specific as the
particular distribution of entities, as neural models tend to overfit by
memorizing properties of frequent entities in a dataset. We tackle the problem
of building robust entity linking models that generalize effectively and do not
rely on labeled entity linking data with a specific entity distribution. Rather
than predicting entities directly, our approach models fine-grained entity
properties, which can help disambiguate between even closely related entities.
We derive a large inventory of types (tens of thousands) from Wikipedia
categories, and use hyperlinked mentions in Wikipedia to distantly label data
and train an entity typing model. At test time, we classify a mention with this
typing model and use soft type predictions to link the mention to the most
similar candidate entity. We evaluate our entity linking system on the
CoNLL-YAGO dataset (Hoffart et al., 2011) and show that our approach
outperforms prior domain-independent entity linking systems. We also test our
approach in a harder setting derived from the WikilinksNED dataset (Eshel et
al., 2017) where all the mention-entity pairs are unseen during test time.
Results indicate that our approach generalizes better than a state-of-the-art
neural model on the dataset.
| 2,020 | Computation and Language |
Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning | Multi-hop QA requires a model to connect multiple pieces of evidence
scattered in a long context to answer the question. The recently proposed
HotpotQA (Yang et al., 2018) dataset is comprised of questions embodying four
different multi-hop reasoning paradigms (two bridge entity setups, checking
multiple properties, and comparing two entities), making it challenging for a
single neural network to handle all four. In this work, we present an
interpretable, controller-based Self-Assembling Neural Modular Network (Hu et
al., 2017, 2018) for multi-hop reasoning, where we design four novel modules
(Find, Relocate, Compare, NoOp) to perform unique types of language reasoning.
Based on a question, our layout controller RNN dynamically infers a series of
reasoning modules to construct the entire network. Empirically, we show that
our dynamic, multi-hop modular network achieves significant improvements over
the static, single-hop baseline (on both regular and adversarial evaluation).
We further demonstrate the interpretability of our model via three analyses.
First, the controller can softly decompose the multi-hop question into multiple
single-hop sub-questions to promote compositional reasoning behavior of the
main network. Second, the controller can predict layouts that conform to the
layouts designed by human experts. Finally, the intermediate module can infer
the entity that connects two distantly-located supporting facts by addressing
the sub-question from the controller.
| 2,019 | Computation and Language |
Query Obfuscation Semantic Decomposition | We propose a method to protect the privacy of search engine users by
decomposing the queries using semantically \emph{related} and unrelated
\emph{distractor} terms. Instead of a single query, the search engine receives
multiple decomposed query terms. Next, we reconstruct the search results
relevant to the original query term by aggregating the search results retrieved
for the decomposed query terms. We show that the word embeddings learnt using a
distributed representation learning method can be used to find semantically
related and distractor query terms. We derive the relationship between the
\emph{obfuscity} achieved through the proposed query anonymisation method and
the \emph{reconstructability} of the original search results using the
decomposed queries. We analytically study the risk of discovering the search
engine users' information intents under the proposed query obfuscation method,
and empirically evaluate its robustness against clustering-based attacks. Our
experimental results show that the proposed method can accurately reconstruct
the search results for user queries, without compromising the privacy of the
search engine users.
| 2,022 | Computation and Language |
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT | Transformer based architectures have become de-facto models used for a range
of Natural Language Processing tasks. In particular, the BERT based models
achieved significant accuracy gain for GLUE tasks, CoNLL-03 and SQuAD. However,
BERT based models have a prohibitive memory footprint and latency. As a result,
deploying BERT based models in resource constrained environments has become a
challenging task. In this work, we perform an extensive analysis of fine-tuned
BERT models using second order Hessian information, and we use our results to
propose a novel method for quantizing BERT models to ultra low precision. In
particular, we propose a new group-wise quantization scheme, and we use a
Hessian based mix-precision method to compress the model further. We
extensively test our proposed method on BERT downstream tasks of SST-2, MNLI,
CoNLL-03, and SQuAD. We can achieve comparable performance to baseline with at
most $2.3\%$ performance degradation, even with ultra-low precision
quantization down to 2 bits, corresponding up to $13\times$ compression of the
model parameters, and up to $4\times$ compression of the embedding table as
well as activations. Among all tasks, we observed the highest performance loss
for BERT fine-tuned on SQuAD. By probing into the Hessian based analysis as
well as visualization, we show that this is related to the fact that current
training/fine-tuning strategy of BERT does not converge for SQuAD.
| 2,020 | Computation and Language |
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided
Dialogue Dataset | Virtual assistants such as Google Assistant, Alexa and Siri provide a
conversational interface to a large number of services and APIs spanning
multiple domains. Such systems need to support an ever-increasing number of
services with possibly overlapping functionality. Furthermore, some of these
services have little to no training data available. Existing public datasets
for task-oriented dialogue do not sufficiently capture these challenges since
they cover few domains and assume a single static ontology per domain. In this
work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing
over 16k multi-domain conversations spanning 16 domains. Our dataset exceeds
the existing task-oriented dialogue corpora in scale, while also highlighting
the challenges associated with building large-scale virtual assistants. It
provides a challenging testbed for a number of tasks including language
understanding, slot filling, dialogue state tracking and response generation.
Along the same lines, we present a schema-guided paradigm for task-oriented
dialogue, in which predictions are made over a dynamic set of intents and
slots, provided as input, using their natural language descriptions. This
allows a single dialogue system to easily support a large number of services
and facilitates simple integration of new services without requiring additional
training data. Building upon the proposed paradigm, we release a model for
dialogue state tracking capable of zero-shot generalization to new APIs, while
remaining competitive in the regular setting.
| 2,020 | Computation and Language |
CTRL: A Conditional Transformer Language Model for Controllable
Generation | Large-scale language models show promising text generation capabilities, but
users cannot easily control particular aspects of the generated text. We
release CTRL, a 1.63 billion-parameter conditional transformer language model,
trained to condition on control codes that govern style, content, and
task-specific behavior. Control codes were derived from structure that
naturally co-occurs with raw text, preserving the advantages of unsupervised
learning while providing more explicit control over text generation. These
codes also allow CTRL to predict which parts of the training data are most
likely given a sequence. This provides a potential method for analyzing large
amounts of data via model-based source attribution. We have released multiple
full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.
| 2,019 | Computation and Language |
Finding Generalizable Evidence by Learning to Convince Q&A Models | We propose a system that finds the strongest supporting evidence for a given
answer to a question, using passage-based question-answering (QA) as a testbed.
We train evidence agents to select the passage sentences that most convince a
pretrained QA model of a given answer, if the QA model received those sentences
instead of the full passage. Rather than finding evidence that convinces one
model alone, we find that agents select evidence that generalizes; agent-chosen
evidence increases the plausibility of the supported answer, as judged by other
QA models and humans. Given its general nature, this approach improves QA in a
robust manner: using agent-selected evidence (i) humans can correctly answer
questions with only ~20% of the full passage and (ii) QA models can generalize
to longer passages and harder questions.
| 2,019 | Computation and Language |
Analyzing machine-learned representations: A natural language case study | As modern deep networks become more complex, and get closer to human-like
capabilities in certain domains, the question arises of how the representations
and decision rules they learn compare to the ones in humans. In this work, we
study representations of sentences in one such artificial system for natural
language processing. We first present a diagnostic test dataset to examine the
degree of abstract composable structure represented. Analyzing performance on
these diagnostic tests indicates a lack of systematicity in the representations
and decision rules, and reveals a set of heuristic strategies. We then
investigate the effect of the training distribution on learning these heuristic
strategies, and study changes in these representations with various
augmentations to the training set. Our results reveal parallels to the
analogous representations in people. We find that these systems can learn
abstract rules and generalize them to new contexts under certain circumstances
-- similar to human zero-shot reasoning. However, we also note some
shortcomings in this generalization behavior -- similar to human judgment
errors like belief bias. Studying these parallels suggests new ways to
understand psychological phenomena in humans as well as informs best strategies
for building artificial intelligence with human-like language understanding.
| 2,019 | Computation and Language |
Sequence-to-sequence Pre-training with Data Augmentation for Sentence
Rewriting | We study sequence-to-sequence (seq2seq) pre-training with data augmentation
for sentence rewriting. Instead of training a seq2seq model with gold training
data and augmented data simultaneously, we separate them to train in different
phases: pre-training with the augmented data and fine-tuning with the gold
data. We also introduce multiple data augmentation methods to help model
pre-training for sentence rewriting. We evaluate our approach in two typical
well-defined sentence rewriting tasks: Grammatical Error Correction (GEC) and
Formality Style Transfer (FST). Experiments demonstrate our approach can better
utilize augmented data without hurting the model's trust in gold data and
further improve the model's performance with our proposed data augmentation
methods.
Our approach substantially advances the state-of-the-art results in
well-recognized sentence rewriting benchmarks over both GEC and FST.
Specifically, it pushes the CoNLL-2014 benchmark's $F_{0.5}$ score and JFLEG
Test GLEU score to 62.61 and 63.54 in the restricted training setting, 66.77
and 65.22 respectively in the unrestricted setting, and advances GYAFC
benchmark's BLEU to 74.24 (2.23 absolute improvement) in E&M domain and 77.97
(2.64 absolute improvement) in F&R domain.
| 2,019 | Computation and Language |
Leveraging 2-hop Distant Supervision from Table Entity Pairs for
Relation Extraction | Distant supervision (DS) has been widely used to automatically construct
(noisy) labeled data for relation extraction (RE). Given two entities, distant
supervision exploits sentences that directly mention them for predicting their
semantic relation. We refer to this strategy as 1-hop DS, which unfortunately
may not work well for long-tail entities with few supporting sentences. In this
paper, we introduce a new strategy named 2-hop DS to enhance distantly
supervised RE, based on the observation that there exist a large number of
relational tables on the Web which contain entity pairs that share common
relations. We refer to such entity pairs as anchors for each other, and collect
all sentences that mention the anchor entity pairs of a given target entity
pair to help relation prediction. We develop a new neural RE method REDS2 in
the multi-instance learning paradigm, which adopts a hierarchical model
structure to fuse information respectively from 1-hop DS and 2-hop DS.
Extensive experimental results on a benchmark dataset show that REDS2 can
consistently outperform various baselines across different settings by a
substantial margin.
| 2,020 | Computation and Language |
Say What I Want: Towards the Dark Side of Neural Dialogue Models | Neural dialogue models have been widely adopted in various chatbot
applications because of their good performance in simulating and generalizing
human conversations. However, there exists a dark side of these models -- due
to the vulnerability of neural networks, a neural dialogue model can be
manipulated by users to say what they want, which brings in concerns about the
security of practical chatbot services. In this work, we investigate whether we
can craft inputs that lead a well-trained black-box neural dialogue model to
generate targeted outputs. We formulate this as a reinforcement learning (RL)
problem and train a Reverse Dialogue Generator which efficiently finds such
inputs for targeted outputs. Experiments conducted on a representative neural
dialogue model show that our proposed model is able to discover such desired
inputs in a considerable portion of cases. Overall, our work reveals this
weakness of neural dialogue models and may prompt further researches of
developing corresponding solutions to avoid it.
| 2,019 | Computation and Language |
Neural Correction Model for Open-Domain Named Entity Recognition | Named Entity Recognition (NER) plays an important role in a wide range of
natural language processing tasks, such as relation extraction, question
answering, etc. However, previous studies on NER are limited to particular
genres, using small manually-annotated or large but low-quality datasets.
Meanwhile, previous datasets for open-domain NER, built using distant
supervision, suffer from low precision, recall and ratio of annotated tokens
(RAT). In this work, to address the low precision and recall problems, we first
utilize DBpedia as the source of distant supervision to annotate abstracts from
Wikipedia and design a neural correction model trained with a human-annotated
NER dataset, DocRED, to correct the false entity labels. In this way, we build
a large and high-quality dataset called AnchorNER and then train various models
with it. To address the low RAT problem of previous datasets, we introduce a
multi-task learning method to exploit the context information. We evaluate our
methods on five NER datasets and our experimental results show that models
trained with AnchorNER and our multi-task learning method obtain
state-of-the-art performances in the open-domain setting.
| 2,020 | Computation and Language |
Neural Machine Translation with 4-Bit Precision and Beyond | Neural Machine Translation (NMT) is resource intensive. We design a
quantization procedure to compress NMT models better for devices with limited
hardware capability. Because most neural network parameters are near zero, we
employ logarithmic quantization in lieu of fixed-point quantization. However,
we find bias terms are less amenable to log quantization but note they comprise
a tiny fraction of the model, so we leave them uncompressed. We also propose to
use an error-feedback mechanism during retraining, to preserve the compressed
model as a stale gradient. We empirically show that NMT models based on
Transformer or RNN architecture can be compressed up to 4-bit precision without
any noticeable quality degradation. Models can be compressed up to binary
precision, albeit with lower quality. The RNN architecture seems to be more
robust to quantization, compared to the Transformer.
| 2,019 | Computation and Language |
A General Framework for Implicit and Explicit Debiasing of
Distributional Word Vector Spaces | Distributional word vectors have recently been shown to encode many of the
human biases, most notably gender and racial biases, and models for attenuating
such biases have consequently been proposed. However, existing models and
studies (1) operate on under-specified and mutually differing bias definitions,
(2) are tailored for a particular bias (e.g., gender bias) and (3) have been
evaluated inconsistently and non-rigorously. In this work, we introduce a
general framework for debiasing word embeddings. We operationalize the
definition of a bias by discerning two types of bias specification: explicit
and implicit. We then propose three debiasing models that operate on explicit
or implicit bias specifications and that can be composed towards more robust
debiasing. Finally, we devise a full-fledged evaluation framework in which we
couple existing bias metrics with newly proposed ones. Experimental findings
across three embedding methods suggest that the proposed debiasing models are
robust and widely applicable: they often completely remove the bias both
implicitly and explicitly without degradation of semantic information encoded
in any of the input distributional spaces. Moreover, we successfully transfer
debiasing models, by means of cross-lingual embedding spaces, and remove or
attenuate biases in distributional word vector spaces of languages that lack
readily available bias specifications.
| 2,020 | Computation and Language |
PubMedQA: A Dataset for Biomedical Research Question Answering | We introduce PubMedQA, a novel biomedical question answering (QA) dataset
collected from PubMed abstracts. The task of PubMedQA is to answer research
questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial
fibrillation after coronary artery bypass grafting?) using the corresponding
abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k
artificially generated QA instances. Each PubMedQA instance is composed of (1)
a question which is either an existing research article title or derived from
one, (2) a context which is the corresponding abstract without its conclusion,
(3) a long answer, which is the conclusion of the abstract and, presumably,
answers the research question, and (4) a yes/no/maybe answer which summarizes
the conclusion. PubMedQA is the first QA dataset where reasoning over
biomedical research texts, especially their quantitative contents, is required
to answer the questions. Our best performing model, multi-phase fine-tuning of
BioBERT with long answer bag-of-word statistics as additional supervision,
achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy
and majority-baseline of 55.2% accuracy, leaving much room for improvement.
PubMedQA is publicly available at https://pubmedqa.github.io.
| 2,019 | Computation and Language |
Neural Architectures for Fine-Grained Propaganda Detection in News | This paper describes our system (MIC-CIS) details and results of
participation in the fine-grained propaganda detection shared task 2019. To
address the tasks of sentence (SLC) and fragment level (FLC) propaganda
detection, we explore different neural architectures (e.g., CNN, LSTM-CRF and
BERT) and extract linguistic (e.g., part-of-speech, named entity, readability,
sentiment, emotion, etc.), layout and topical features. Specifically, we have
designed multi-granularity and multi-tasking neural architectures to jointly
perform both the sentence and fragment level propaganda detection.
Additionally, we investigate different ensemble schemes such as
majority-voting, relax-voting, etc. to boost overall system performance.
Compared to the other participating systems, our submissions are ranked 3rd and
4th in FLC and SLC tasks, respectively.
| 2,019 | Computation and Language |
Style-aware Neural Model with Application in Authorship Attribution | Writing style is a combination of consistent decisions associated with a
specific author at different levels of language production, including lexical,
syntactic, and structural. In this paper, we introduce a style-aware neural
model to encode document information from three stylistic levels and evaluate
it in the domain of authorship attribution. First, we propose a simple way to
jointly encode syntactic and lexical representations of sentences.
Subsequently, we employ an attention-based hierarchical neural network to
encode the syntactic and semantic structure of sentences in documents while
rewarding the sentences which contribute more to capturing the writing style.
Our experimental results, based on four benchmark datasets, reveal the benefits
of encoding document information from all three stylistic levels when compared
to the baseline methods in the literature.
| 2,019 | Computation and Language |
A Neural Approach to Irony Generation | Ironies can not only express stronger emotions but also show a sense of
humor. With the development of social media, ironies are widely used in public.
Although many prior research studies have been conducted in irony detection,
few studies focus on irony generation. The main challenges for irony generation
are the lack of large-scale irony dataset and difficulties in modeling the
ironic pattern. In this work, we first systematically define irony generation
based on style transfer task. To address the lack of data, we make use of
twitter and build a large-scale dataset. We also design a combination of
rewards for reinforcement learning to control the generation of ironic
sentences. Experimental results demonstrate the effectiveness of our model in
terms of irony accuracy, sentiment preservation, and content preservation.
| 2,019 | Computation and Language |
Taxonomical hierarchy of canonicalized relations from multiple Knowledge
Bases | This work addresses two important questions pertinent to Relation Extraction
(RE). First, what are all possible relations that could exist between any two
given entity types? Second, how do we define an unambiguous taxonomical (is-a)
hierarchy among the identified relations? To address the first question, we use
three resources Wikipedia Infobox, Wikidata, and DBpedia. This study focuses on
relations between person, organization and location entity types. We exploit
Wikidata and DBpedia in a data-driven manner, and Wikipedia Infobox templates
manually to generate lists of relations. Further, to address the second
question, we canonicalize, filter, and combine the identified relations from
the three resources to construct a taxonomical hierarchy. This hierarchy
contains 623 canonical relations with highest contribution from Wikipedia
Infobox followed by DBpedia and Wikidata. The generated relation list subsumes
an average of 85% of relations from RE datasets when entity types are
restricted.
| 2,019 | Computation and Language |
Scene Graph Parsing by Attention Graph | Scene graph representations, which form a graph of visual object nodes
together with their attributes and relations, have proved useful across a
variety of vision and language applications. Recent work in the area has used
Natural Language Processing dependency tree methods to automatically build
scene graphs.
In this work, we present an 'Attention Graph' mechanism that can be trained
end-to-end, and produces a scene graph structure that can be lifted directly
from the top layer of a standard Transformer model.
The scene graphs generated by our model achieve an F-score similarity of
52.21% to ground-truth graphs on the evaluation set using the SPICE metric,
surpassing the best previous approaches by 2.5%.
| 2,019 | Computation and Language |
Parameterized Convolutional Neural Networks for Aspect Level Sentiment
Classification | We introduce a novel parameterized convolutional neural network for aspect
level sentiment classification. Using parameterized filters and parameterized
gates, we incorporate aspect information into convolutional neural networks
(CNN). Experiments demonstrate that our parameterized filters and parameterized
gates effectively capture the aspect-specific features, and our CNN-based
models achieve excellent results on SemEval 2014 datasets.
| 2,019 | Computation and Language |
Toward Automated Quest Generation in Text-Adventure Games | Interactive fictions, or text-adventures, are games in which a player
interacts with a world entirely through textual descriptions and text actions.
Text-adventure games are typically structured as puzzles or quests wherein the
player must execute certain actions in a certain order to succeed. In this
paper, we consider the problem of procedurally generating a quest, defined as a
series of actions required to progress towards a goal, in a text-adventure
game. Quest generation in text environments is challenging because they must be
semantically coherent. We present and evaluate two quest generation techniques:
(1) a Markov model, and (2) a neural generative model. We specifically look at
generating quests about cooking and train our models on recipe data. We
evaluate our techniques with human participant studies looking at perceived
creativity and coherence.
| 2,020 | Computation and Language |
A Comparative Study on Transformer vs RNN in Speech Applications | Sequence-to-sequence models have been widely used in end-to-end speech
processing, for example, automatic speech recognition (ASR), speech translation
(ST), and text-to-speech (TTS). This paper focuses on an emergent
sequence-to-sequence model called Transformer, which achieves state-of-the-art
performance in neural machine translation and other natural language processing
applications. We undertook intensive studies in which we experimentally
compared and analyzed Transformer and conventional recurrent neural networks
(RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS
benchmarks. Our experiments revealed various training tips and significant
performance benefits obtained with Transformer for each task including the
surprising superiority of Transformer in 13/15 ASR benchmarks in comparison
with RNN. We are preparing to release Kaldi-style reproducible recipes using
open source and publicly available datasets for all the ASR, ST, and TTS tasks
for the community to succeed our exciting outcomes.
| 2,019 | Computation and Language |
End-to-End Bias Mitigation by Modelling Biases in Corpora | Several recent studies have shown that strong natural language understanding
(NLU) models are prone to relying on unwanted dataset biases without learning
the underlying task, resulting in models that fail to generalize to
out-of-domain datasets and are likely to perform poorly in real-world
scenarios. We propose two learning strategies to train neural models, which are
more robust to such biases and transfer better to out-of-domain datasets. The
biases are specified in terms of one or more bias-only models, which learn to
leverage the dataset biases. During training, the bias-only models' predictions
are used to adjust the loss of the base model to reduce its reliance on biases
by down-weighting the biased examples and focusing the training on the hard
examples. We experiment on large-scale natural language inference and fact
verification benchmarks, evaluating on out-of-domain datasets that are
specifically designed to assess the robustness of models against known biases
in the training data. Results show that our debiasing methods greatly improve
robustness in all settings and better transfer to other textual entailment
datasets. Our code and data are publicly available in
\url{https://github.com/rabeehk/robust-nli}.
| 2,020 | Computation and Language |
Addressing Semantic Drift in Question Generation for Semi-Supervised
Question Answering | Text-based Question Generation (QG) aims at generating natural and relevant
questions that can be answered by a given answer in some context. Existing QG
models suffer from a "semantic drift" problem, i.e., the semantics of the
model-generated question drifts away from the given context and answer. In this
paper, we first propose two semantics-enhanced rewards obtained from downstream
question paraphrasing and question answering tasks to regularize the QG model
to generate semantically valid questions. Second, since the traditional
evaluation metrics (e.g., BLEU) often fall short in evaluating the quality of
generated questions, we propose a QA-based evaluation method which measures the
QG model's ability to mimic human annotators in generating QA training data.
Experiments show that our method achieves the new state-of-the-art performance
w.r.t. traditional metrics, and also performs best on our QA-based evaluation
metrics. Further, we investigate how to use our QG model to augment QA datasets
and enable semi-supervised QA. We propose two ways to generate synthetic QA
pairs: generate new questions from existing articles or collect QA pairs from
new articles. We also propose two empirically effective strategies, a data
filter and mixing mini-batch training, to properly use the QG-generated data
for QA. Experiments show that our method improves over both BiDAF and BERT QA
baselines, even without introducing new articles.
| 2,019 | Computation and Language |
Learning Household Task Knowledge from WikiHow Descriptions | Commonsense procedural knowledge is important for AI agents and robots that
operate in a human environment. While previous attempts at constructing
procedural knowledge are mostly rule- and template-based, recent advances in
deep learning provide the possibility of acquiring such knowledge directly from
natural language sources. As a first step in this direction, we propose a model
to learn embeddings for tasks, as well as the individual steps that need to be
taken to solve them, based on WikiHow articles. We learn these embeddings such
that they are predictive of both step relevance and step ordering. We also
experiment with the use of integer programming for inferring consistent global
step orderings from noisy pairwise predictions.
| 2,019 | Computation and Language |
Multi-class Multilingual Classification of Wikipedia Articles Using
Extended Named Entity Tag Set | Wikipedia is a great source of general world knowledge which can guide NLP
models better understand their motivation to make predictions. Structuring
Wikipedia is the initial step towards this goal which can facilitate fine-grain
classification of articles. In this work, we introduce the Shinra 5-Language
Categorization Dataset (SHINRA-5LDS), a large multi-lingual and multi-labeled
set of annotated Wikipedia articles in Japanese, English, French, German, and
Farsi using Extended Named Entity (ENE) tag set. We evaluate the dataset using
the best models provided for ENE label set classification and show that the
currently available classification models struggle with large datasets using
fine-grained tag sets.
| 2,020 | Computation and Language |
Harnessing Indirect Training Data for End-to-End Automatic Speech
Translation: Tricks of the Trade | For automatic speech translation (AST), end-to-end approaches are
outperformed by cascaded models that transcribe with automatic speech
recognition (ASR), then translate with machine translation (MT). A major cause
of the performance gap is that, while existing AST corpora are small, massive
datasets exist for both the ASR and MT subsystems. In this work, we evaluate
several data augmentation and pretraining approaches for AST, by comparing all
on the same datasets. Simple data augmentation by translating ASR transcripts
proves most effective on the English--French augmented LibriSpeech dataset,
closing the performance gap from 8.2 to 1.4 BLEU, compared to a very strong
cascade that could directly utilize copious ASR and MT data. The same
end-to-end approach plus fine-tuning closes the gap on the English--Romanian
MuST-C dataset from 6.7 to 3.7 BLEU. In addition to these results, we present
practical recommendations for augmentation and pretraining approaches. Finally,
we decrease the performance gap to 0.01 BLEU using a Transformer-based
architecture.
| 2,019 | Computation and Language |
A Universal Parent Model for Low-Resource Neural Machine Translation
Transfer | Transfer learning from a high-resource language pair `parent' has been proven
to be an effective way to improve neural machine translation quality for
low-resource language pairs `children.' However, previous approaches build a
custom parent model or at least update an existing parent model's vocabulary
for each child language pair they wish to train, in an effort to align parent
and child vocabularies. This is not a practical solution. It is wasteful to
devote the majority of training time for new language pairs to optimizing
parameters on an unrelated data set. Further, this overhead reduces the utility
of neural machine translation for deployment in humanitarian assistance
scenarios, where extra time to deploy a new language pair can mean the
difference between life and death. In this work, we present a `universal'
pre-trained neural parent model with constant vocabulary that can be used as a
starting point for training practically any new low-resource language to a
fixed target language. We demonstrate that our approach, which leverages
orthography unification and a broad-coverage approach to subword
identification, generalizes well to several languages from a variety of
families, and that translation systems built with our approach can be built
more quickly than competing methods and with better quality as well.
| 2,019 | Computation and Language |
Multi-view and Multi-source Transfers in Neural Topic Modeling with
Pretrained Topic and Word Embeddings | Though word embeddings and topics are complementary representations, several
past works have only used pre-trained word embeddings in (neural) topic
modeling to address data sparsity problem in short text or small collection of
documents. However, no prior work has employed (pre-trained latent) topics in
transfer learning paradigm. In this paper, we propose an approach to (1)
perform knowledge transfer using latent topics obtained from a large source
corpus, and (2) jointly transfer knowledge via the two representations (or
views) in neural topic modeling to improve topic quality, better deal with
polysemy and data sparsity issues in a target corpus. In doing so, we first
accumulate topics and word representations from one or many source corpora to
build a pool of topics and word vectors. Then, we identify one or multiple
relevant source domain(s) and take advantage of corresponding topics and word
features via the respective pools to guide meaningful learning in the sparse
target domain. We quantify the quality of topic and document representations
via generalization (perplexity), interpretability (topic coherence) and
information retrieval (IR) using short-text, long-text, small and large
document collections from news and medical domains. We have demonstrated the
state-of-the-art results on topic modeling with the proposed framework.
| 2,019 | Computation and Language |
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation | In this paper, we describe ALTER, an auxiliary text rewriting tool that
facilitates the rewriting process for natural language generation tasks, such
as paraphrasing, text simplification, fairness-aware text rewriting, and text
style transfer. Our tool is characterized by two features, i) recording of
word-level revision histories and ii) flexible auxiliary edit support and
feedback to annotators. The text rewriting assist and traceable rewriting
history are potentially beneficial to the future research of natural language
generation.
| 2,019 | Computation and Language |
Efficiency Metrics for Data-Driven Models: A Text Summarization Case
Study | Using data-driven models for solving text summarization or similar tasks has
become very common in the last years. Yet most of the studies report basic
accuracy scores only, and nothing is known about the ability of the proposed
models to improve when trained on more data. In this paper, we define and
propose three data efficiency metrics: data score efficiency, data time
deficiency and overall data efficiency. We also propose a simple scheme that
uses those metrics and apply it for a more comprehensive evaluation of popular
methods on text summarization and title generation tasks. For the latter task,
we process and release a huge collection of 35 million abstract-title pairs
from scientific articles. Our results reveal that among the tested models, the
Transformer is the most efficient on both tasks.
| 2,020 | Computation and Language |
Tree Transformer: Integrating Tree Structures into Self-Attention | Pre-training Transformer from large-scale raw texts and fine-tuning on the
desired task have achieved state-of-the-art results on diverse NLP tasks.
However, it is unclear what the learned attention captures. The attention
computed by attention heads seems not to match human intuitions about
hierarchical structures. This paper proposes Tree Transformer, which adds an
extra constraint to attention heads of the bidirectional Transformer encoder in
order to encourage the attention heads to follow tree structures. The tree
structures can be automatically induced from raw texts by our proposed
"Constituent Attention" module, which is simply implemented by self-attention
between two adjacent words. With the same training procedure identical to BERT,
the experiments demonstrate the effectiveness of Tree Transformer in terms of
inducing tree structures, better language modeling, and further learning more
explainable attention scores.
| 2,019 | Computation and Language |
Current Challenges in Spoken Dialogue Systems and Why They Are Critical
for Those Living with Dementia | Dialogue technologies such as Amazon's Alexa have the potential to transform
the healthcare industry. However, current systems are not yet naturally
interactive: they are often turn-based, have naive end-of-turn detection and
completely ignore many types of verbal and visual feedback - such as
backchannels, hesitation markers, filled pauses, gaze, brow furrows and
disfluencies - that are crucial in guiding and managing the conversational
process. This is especially important in the healthcare industry as target
users of Spoken Dialogue Systems (SDSs) are likely to be frail, older,
distracted or suffer from cognitive decline which impacts their ability to make
effective use of current systems. In this paper, we outline some of the
challenges that are in urgent need of further research, including Incremental
Speech Recognition and a systematic study of the interactional patterns in
conversation that are potentially diagnostic of dementia, and how these might
inform research on and the design of the next generation of SDSs.
| 2,019 | Computation and Language |
Beyond BLEU: Training Neural Machine Translation with Semantic
Similarity | While most neural machine translation (NMT) systems are still trained using
maximum likelihood estimation, recent work has demonstrated that optimizing
systems to directly improve evaluation metrics such as BLEU can substantially
improve final translation accuracy. However, training with BLEU has some
limitations: it doesn't assign partial credit, it has a limited range of output
values, and it can penalize semantically correct hypotheses if they differ
lexically from the reference. In this paper, we introduce an alternative reward
function for optimizing NMT systems that is based on recent work in semantic
similarity. We evaluate on four disparate languages translated to English, and
find that training with our proposed metric results in better translations as
evaluated by BLEU, semantic similarity, and human evaluation, and also that the
optimization procedure converges faster. Analysis suggests that this is because
the proposed metric is more conducive to optimization, assigning partial credit
and providing more diversity in scores than BLEU.
| 2,019 | Computation and Language |
Ouroboros: On Accelerating Training of Transformer-Based Language Models | Language models are essential for natural language processing (NLP) tasks,
such as machine translation and text summarization. Remarkable performance has
been demonstrated recently across many NLP domains via a Transformer-based
language model with over a billion parameters, verifying the benefits of model
size. Model parallelism is required if a model is too large to fit in a single
computing device. Current methods for model parallelism either suffer from
backward locking in backpropagation or are not applicable to language models.
We propose the first model-parallel algorithm that speeds the training of
Transformer-based language models. We also prove that our proposed algorithm is
guaranteed to converge to critical points for non-convex problems. Extensive
experiments on Transformer and Transformer-XL language models demonstrate that
the proposed algorithm obtains a much faster speedup beyond data parallelism,
with comparable or better accuracy. Code to reproduce experiments is to be
found at \url{https://github.com/LaraQianYang/Ouroboros}.
| 2,019 | Computation and Language |
Hint-Based Training for Non-Autoregressive Machine Translation | Due to the unparallelizable nature of the autoregressive factorization,
AutoRegressive Translation (ART) models have to generate tokens sequentially
during decoding and thus suffer from high inference latency. Non-AutoRegressive
Translation (NART) models were proposed to reduce the inference time, but could
only achieve inferior translation accuracy. In this paper, we proposed a novel
approach to leveraging the hints from hidden states and word alignments to help
the training of NART models. The results achieve significant improvement over
previous NART models for the WMT14 En-De and De-En datasets and are even
comparable to a strong LSTM-based ART baseline but one order of magnitude
faster in inference.
| 2,019 | Computation and Language |
Natural Language Adversarial Defense through Synonym Encoding | In the area of natural language processing, deep learning models are recently
known to be vulnerable to various types of adversarial perturbations, but
relatively few works are done on the defense side. Especially, there exists few
effective defense method against the successful synonym substitution based
attacks that preserve the syntactic structure and semantic information of the
original text while fooling the deep learning models. We contribute in this
direction and propose a novel adversarial defense method called Synonym
Encoding Method (SEM). Specifically, SEM inserts an encoder before the input
layer of the target model to map each cluster of synonyms to a unique encoding
and trains the model to eliminate possible adversarial perturbations without
modifying the network architecture or adding extra data. Extensive experiments
demonstrate that SEM can effectively defend the current synonym substitution
based attacks and block the transferability of adversarial examples. SEM is
also easy and efficient to scale to large models and big datasets.
| 2,021 | Computation and Language |
Emu: Enhancing Multilingual Sentence Embeddings with Semantic
Specialization | We present Emu, a system that semantically enhances multilingual sentence
embeddings. Our framework fine-tunes pre-trained multilingual sentence
embeddings using two main components: a semantic classifier and a language
discriminator. The semantic classifier improves the semantic similarity of
related sentences, whereas the language discriminator enhances the
multilinguality of the embeddings via multilingual adversarial training. Our
experimental results based on several language pairs show that our specialized
embeddings outperform the state-of-the-art multilingual sentence embedding
model on the task of cross-lingual intent classification using only monolingual
labeled data.
| 2,019 | Computation and Language |
Learning Rhyming Constraints using Structured Adversaries | Existing recurrent neural language models often fail to capture higher-level
structure present in text: for example, rhyming patterns present in poetry.
Much prior work on poetry generation uses manually defined constraints which
are satisfied during decoding using either specialized decoding procedures or
rejection sampling. The rhyming constraints themselves are typically not
learned by the generator. We propose an alternate approach that uses a
structured discriminator to learn a poetry generator that directly captures
rhyming constraints in a generative adversarial setup. By causing the
discriminator to compare poems based only on a learned similarity matrix of
pairs of line ending words, the proposed approach is able to successfully learn
rhyming patterns in two different English poetry datasets (Sonnet and Limerick)
without explicitly being provided with any phonetic information.
| 2,019 | Computation and Language |
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB
Retriever | Querying the knowledge base (KB) has long been a challenge in the end-to-end
task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue
generation work treats the KB query as an attention over the entire KB, without
the guarantee that the generated entities are consistent with each other. In
this paper, we propose a novel framework which queries the KB in two steps to
improve the consistency of generated entities. In the first step, inspired by
the observation that a response can usually be supported by a single KB row, we
introduce a KB retrieval component which explicitly returns the most relevant
KB row given a dialogue history. The retrieval result is further used to filter
the irrelevant entities in a Seq2Seq response generation model to improve the
consistency among the output entities. In the second step, we further perform
the attention mechanism to address the most correlated KB column. Two methods
are proposed to make the training feasible without labeled retrieval data,
which include distant supervision and Gumbel-Softmax technique. Experiments on
two publicly available task oriented dialog datasets show the effectiveness of
our model by outperforming the baseline systems and producing entity-consistent
responses.
| 2,019 | Computation and Language |
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing | This paper investigates the problem of learning cross-lingual representations
in a contextual space. We propose Cross-Lingual BERT Transformation (CLBT), a
simple and efficient approach to generate cross-lingual contextualized word
embeddings based on publicly available pre-trained BERT models (Devlin et al.,
2018). In this approach, a linear transformation is learned from contextual
word alignments to align the contextualized embeddings independently trained in
different languages. We demonstrate the effectiveness of this approach on
zero-shot cross-lingual transfer parsing. Experiments show that our embeddings
substantially outperform the previous state-of-the-art that uses static
embeddings. We further compare our approach with XLM (Lample and Conneau,
2019), a recently proposed cross-lingual language model trained with massive
parallel data, and achieve highly competitive results.
| 2,019 | Computation and Language |
Automatically Extracting Challenge Sets for Non local Phenomena in
Neural Machine Translation | We show that the state of the art Transformer Machine Translation (MT) model
is not biased towards monotonic reordering (unlike previous recurrent neural
network models), but that nevertheless, long-distance dependencies remain a
challenge for the model. Since most dependencies are short-distance, common
evaluation metrics will be little influenced by how well systems perform on
them. We, therefore, propose an automatic approach for extracting challenge
sets replete with long-distance dependencies and argue that evaluation using
this methodology provides a complementary perspective on system performance. To
support our claim, we compile challenge sets for English-German and
German-English, which are much larger than any previously released challenge
set for MT. The extracted sets are large enough to allow reliable automatic
evaluation, which makes the proposed approach a scalable and practical solution
for evaluating MT performance on the long-tail of syntactic phenomena.
| 2,019 | Computation and Language |
Query-Focused Scenario Construction | The news coverage of events often contains not one but multiple incompatible
accounts of what happened. We develop a query-based system that extracts
compatible sets of events (scenarios) from such data, formulated as one-class
clustering. Our system incrementally evaluates each event's compatibility with
already selected events, taking order into account. We use synthetic data
consisting of article mixtures for scalable training and evaluate our model on
a new human-curated dataset of scenarios about real-world news topics. Stronger
neural network models and harder synthetic training settings are both important
to achieve high performance, and our final scenario construction system
substantially outperforms baselines based on prior work.
| 2,019 | Computation and Language |
Temporal Self-Attention Network for Medical Concept Embedding | In longitudinal electronic health records (EHRs), the event records of a
patient are distributed over a long period of time and the temporal relations
between the events reflect sufficient domain knowledge to benefit prediction
tasks such as the rate of inpatient mortality. Medical concept embedding as a
feature extraction method that transforms a set of medical concepts with a
specific time stamp into a vector, which will be fed into a supervised learning
algorithm. The quality of the embedding significantly determines the learning
performance over the medical data. In this paper, we propose a medical concept
embedding method based on applying a self-attention mechanism to represent each
medical concept. We propose a novel attention mechanism which captures the
contextual information and temporal relationships between medical concepts. A
light-weight neural net, "Temporal Self-Attention Network (TeSAN)", is then
proposed to learn medical concept embedding based solely on the proposed
attention mechanism. To test the effectiveness of our proposed methods, we have
conducted clustering and prediction tasks on two public EHRs datasets comparing
TeSAN against five state-of-the-art embedding methods. The experimental results
demonstrate that the proposed TeSAN model is superior to all the compared
methods. To the best of our knowledge, this work is the first to exploit
temporal self-attentive relations between medical events.
| 2,020 | Computation and Language |
CM-Net: A Novel Collaborative Memory Network for Spoken Language
Understanding | Spoken Language Understanding (SLU) mainly involves two tasks, intent
detection and slot filling, which are generally modeled jointly in existing
works. However, most existing models fail to fully utilize co-occurrence
relations between slots and intents, which restricts their potential
performance. To address this issue, in this paper we propose a novel
Collaborative Memory Network (CM-Net) based on the well-designed block, named
CM-block. The CM-block firstly captures slot-specific and intent-specific
features from memories in a collaborative manner, and then uses these enriched
features to enhance local context representations, based on which the
sequential information flow leads to more specific (slot and intent) global
utterance representations. Through stacking multiple CM-blocks, our CM-Net is
able to alternately perform information exchange among specific memories, local
contexts and the global utterance, and thus incrementally enriches each other.
We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a
self-collected corpus (CAIS). Experimental results show that the CM-Net
achieves the state-of-the-art results on the ATIS and SNIPS in most of
criteria, and significantly outperforms the baseline models on the CAIS.
Additionally, we make the CAIS dataset publicly available for the research
community.
| 2,019 | Computation and Language |
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