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Word2vec Conjecture and A Limitative Result
|
Being inspired by the success of \texttt{word2vec}
\citep{mikolov2013distributed} in capturing analogies, we study the conjecture
that analogical relations can be represented by vector spaces. Unlike many
previous works that focus on the distributional semantic aspect of
\texttt{word2vec}, we study the purely \emph{representational} question: can
\emph{all} semantic word-word relations be represented by differences (or
directions) of vectors? We call this the word2vec conjecture and point out some
of its desirable implications. However, we will exhibit a class of relations
that cannot be represented in this way, thus falsifying the conjecture and
establishing a limitative result for the representability of semantic relations
by vector spaces over fields of characteristic 0, e.g., real or complex
numbers.
| 2,020 |
Computation and Language
|
Constrained Abstractive Summarization: Preserving Factual Consistency
with Constrained Generation
|
Despite significant progress, state-of-the-art abstractive summarization
methods are still prone to hallucinate content inconsistent with the source
document. In this paper, we propose Constrained Abstractive Summarization
(CAS), a general setup that preserves the factual consistency of abstractive
summarization by specifying tokens as constraints that must be present in the
summary. We adopt lexically constrained decoding, a technique generally
applicable to autoregressive generative models, to fulfill CAS and conduct
experiments in two scenarios: (1) automatic summarization without human
involvement, where keyphrases are extracted from the source document and used
as constraints; (2) human-guided interactive summarization, where human
feedback in the form of manual constraints are used to guide summary
generation. Automatic and human evaluations on two benchmark datasets
demonstrate that CAS improves both lexical overlap (ROUGE) and factual
consistency of abstractive summarization. In particular, we observe up to 13.8
ROUGE-2 gains when only one manual constraint is used in interactive
summarization.
| 2,021 |
Computation and Language
|
Compositional Generalization and Natural Language Variation: Can a
Semantic Parsing Approach Handle Both?
|
Sequence-to-sequence models excel at handling natural language variation, but
have been shown to struggle with out-of-distribution compositional
generalization. This has motivated new specialized architectures with stronger
compositional biases, but most of these approaches have only been evaluated on
synthetically-generated datasets, which are not representative of natural
language variation. In this work we ask: can we develop a semantic parsing
approach that handles both natural language variation and compositional
generalization? To better assess this capability, we propose new train and test
splits of non-synthetic datasets. We demonstrate that strong existing
approaches do not perform well across a broad set of evaluations. We also
propose NQG-T5, a hybrid model that combines a high-precision grammar-based
approach with a pre-trained sequence-to-sequence model. It outperforms existing
approaches across several compositional generalization challenges on
non-synthetic data, while also being competitive with the state-of-the-art on
standard evaluations. While still far from solving this problem, our study
highlights the importance of diverse evaluations and the open challenge of
handling both compositional generalization and natural language variation in
semantic parsing.
| 2,021 |
Computation and Language
|
ANLIzing the Adversarial Natural Language Inference Dataset
|
We perform an in-depth error analysis of Adversarial NLI (ANLI), a recently
introduced large-scale human-and-model-in-the-loop natural language inference
dataset collected over multiple rounds. We propose a fine-grained annotation
scheme of the different aspects of inference that are responsible for the gold
classification labels, and use it to hand-code all three of the ANLI
development sets. We use these annotations to answer a variety of interesting
questions: which inference types are most common, which models have the highest
performance on each reasoning type, and which types are the most challenging
for state of-the-art models? We hope that our annotations will enable more
fine-grained evaluation of models trained on ANLI, provide us with a deeper
understanding of where models fail and succeed, and help us determine how to
train better models in future.
| 2,020 |
Computation and Language
|
Char2Subword: Extending the Subword Embedding Space Using Robust
Character Compositionality
|
Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword
tokenization process of language models as it provides multiple benefits.
However, this process is solely based on pre-training data statistics, making
it hard for the tokenizer to handle infrequent spellings. On the other hand,
though robust to misspellings, pure character-level models often lead to
unreasonably long sequences and make it harder for the model to learn
meaningful words. To alleviate these challenges, we propose a character-based
subword module (char2subword) that learns the subword embedding table in
pre-trained models like BERT. Our char2subword module builds representations
from characters out of the subword vocabulary, and it can be used as a drop-in
replacement of the subword embedding table. The module is robust to
character-level alterations such as misspellings, word inflection, casing, and
punctuation. We integrate it further with BERT through pre-training while
keeping BERT transformer parameters fixed--and thus, providing a practical
method. Finally, we show that incorporating our module to mBERT significantly
improves the performance on the social media linguistic code-switching
evaluation (LinCE) benchmark.
| 2,021 |
Computation and Language
|
An Evaluation Protocol for Generative Conversational Systems
|
There is a multitude of novel generative models for open-domain
conversational systems; however, there is no systematic evaluation of different
systems. Systematic comparisons require consistency in experimental design,
evaluation sets, conversational systems and their outputs, and statistical
analysis. We lay out a protocol for the evaluation of conversational models
using head-to-head pairwise comparison. We analyze ten recent models that claim
state-of-the-art performance using a paired head-to-head performance
(win-loss-tie) on five evaluation datasets. Our findings show that DialoGPT and
Blender are superior systems using Bradley-Terry model and TrueSkill ranking
methods. These findings demonstrate the feasibility of our protocol to evaluate
conversational agents and evaluation sets. Finally, we make all code and
evaluations publicly available for researchers to compare their model to other
state-of-the-art dialog models.
| 2,020 |
Computation and Language
|
Text Style Transfer: A Review and Experimental Evaluation
|
The stylistic properties of text have intrigued computational linguistics
researchers in recent years. Specifically, researchers have investigated the
Text Style Transfer (TST) task, which aims to change the stylistic properties
of the text while retaining its style independent content. Over the last few
years, many novel TST algorithms have been developed, while the industry has
leveraged these algorithms to enable exciting TST applications. The field of
TST research has burgeoned because of this symbiosis. This article aims to
provide a comprehensive review of recent research efforts on text style
transfer. More concretely, we create a taxonomy to organize the TST models and
provide a comprehensive summary of the state of the art. We review the existing
evaluation methodologies for TST tasks and conduct a large-scale
reproducibility study where we experimentally benchmark 19 state-of-the-art TST
algorithms on two publicly available datasets. Finally, we expand on current
trends and provide new perspectives on the new and exciting developments in the
TST field.
| 2,022 |
Computation and Language
|
Temporal Reasoning on Implicit Events from Distant Supervision
|
We propose TRACIE, a novel temporal reasoning dataset that evaluates the
degree to which systems understand implicit events -- events that are not
mentioned explicitly in natural language text but can be inferred from it. This
introduces a new challenge in temporal reasoning research, where prior work has
focused on explicitly mentioned events. Human readers can infer implicit events
via commonsense reasoning, resulting in a more comprehensive understanding of
the situation and, consequently, better reasoning about time. We find, however,
that state-of-the-art models struggle when predicting temporal relationships
between implicit and explicit events. To address this, we propose a
neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant
supervision signals from large-scale text and uses temporal rules to combine
start times and durations to infer end times. SYMTIME outperforms strong
baseline systems on TRACIE by 5%, and by 11% in a zero prior knowledge training
setting. Our approach also generalizes to other temporal reasoning tasks, as
evidenced by a gain of 1%-9% on MATRES, an explicit event benchmark.
| 2,021 |
Computation and Language
|
Effective Distant Supervision for Temporal Relation Extraction
|
A principal barrier to training temporal relation extraction models in new
domains is the lack of varied, high quality examples and the challenge of
collecting more. We present a method of automatically collecting
distantly-supervised examples of temporal relations. We scrape and
automatically label event pairs where the temporal relations are made explicit
in text, then mask out those explicit cues, forcing a model trained on this
data to learn other signals. We demonstrate that a pre-trained Transformer
model is able to transfer from the weakly labeled examples to human-annotated
benchmarks in both zero-shot and few-shot settings, and that the masking scheme
is important in improving generalization.
| 2,021 |
Computation and Language
|
Adding Chit-Chat to Enhance Task-Oriented Dialogues
|
Existing dialogue corpora and models are typically designed under two
disjoint motives: while task-oriented systems focus on achieving functional
goals (e.g., booking hotels), open-domain chatbots aim at making socially
engaging conversations. In this work, we propose to integrate both types of
systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with
the goal of making virtual assistant conversations more engaging and
interactive. Specifically, we propose a Human <-> AI collaborative data
collection approach for generating diverse chit-chat responses to augment
task-oriented dialogues with minimal annotation effort. We then present our new
chit-chat-based annotations to 23.8K dialogues from two popular task-oriented
datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their
advantage over the originals via human evaluation. Lastly, we propose three new
models for adding chit-chat to task-oriented dialogues, explicitly trained to
predict user goals and to generate contextually relevant chit-chat responses.
Automatic and human evaluations show that, compared with the state-of-the-art
task-oriented baseline, our models can code-switch between task and chit-chat
to be more engaging, interesting, knowledgeable, and humanlike, while
maintaining competitive task performance.
| 2,021 |
Computation and Language
|
NUANCED: Natural Utterance Annotation for Nuanced Conversation with
Estimated Distributions
|
Existing conversational systems are mostly agent-centric, which assumes the
user utterances would closely follow the system ontology (for NLU or dialogue
state tracking). However, in real-world scenarios, it is highly desirable that
the users can speak freely in their own way. It is extremely hard, if not
impossible, for the users to adapt to the unknown system ontology. In this
work, we attempt to build a user-centric dialogue system. As there is no clean
mapping for a user's free form utterance to an ontology, we first model the
user preferences as estimated distributions over the system ontology and map
the users' utterances to such distributions. Learning such a mapping poses new
challenges on reasoning over existing knowledge, ranging from factoid
knowledge, commonsense knowledge to the users' own situations. To this end, we
build a new dataset named NUANCED that focuses on such realistic settings for
conversational recommendation. Collected via dialogue simulation and
paraphrasing, NUANCED contains 5.1k dialogues, 26k turns of high-quality user
responses. We conduct experiments, showing both the usefulness and challenges
of our problem setting. We believe NUANCED can serve as a valuable resource to
push existing research from the agent-centric system to the user-centric
system. The code and data is publicly available at
\url{https://github.com/facebookresearch/nuanced}.
| 2,021 |
Computation and Language
|
Measuring Association Between Labels and Free-Text Rationales
|
In interpretable NLP, we require faithful rationales that reflect the model's
decision-making process for an explained instance. While prior work focuses on
extractive rationales (a subset of the input words), we investigate their
less-studied counterpart: free-text natural language rationales. We demonstrate
that pipelines, existing models for faithful extractive rationalization on
information-extraction style tasks, do not extend as reliably to "reasoning"
tasks requiring free-text rationales. We turn to models that jointly predict
and rationalize, a class of widely used high-performance models for free-text
rationalization whose faithfulness is not yet established. We define
label-rationale association as a necessary property for faithfulness: the
internal mechanisms of the model producing the label and the rationale must be
meaningfully correlated. We propose two measurements to test this property:
robustness equivalence and feature importance agreement. We find that
state-of-the-art T5-based joint models exhibit both properties for
rationalizing commonsense question-answering and natural language inference,
indicating their potential for producing faithful free-text rationales.
| 2,022 |
Computation and Language
|
Modular Networks for Compositional Instruction Following
|
Standard architectures used in instruction following often struggle on novel
compositions of subgoals (e.g. navigating to landmarks or picking up objects)
observed during training. We propose a modular architecture for following
natural language instructions that describe sequences of diverse subgoals. In
our approach, subgoal modules each carry out natural language instructions for
a specific subgoal type. A sequence of modules to execute is chosen by learning
to segment the instructions and predicting a subgoal type for each segment.
When compared to standard, non-modular sequence-to-sequence approaches on
ALFRED, a challenging instruction following benchmark, we find that
modularization improves generalization to novel subgoal compositions, as well
as to environments unseen in training.
| 2,021 |
Computation and Language
|
Conversational Semantic Parsing for Dialog State Tracking
|
We consider a new perspective on dialog state tracking (DST), the task of
estimating a user's goal through the course of a dialog. By formulating DST as
a semantic parsing task over hierarchical representations, we can incorporate
semantic compositionality, cross-domain knowledge sharing and co-reference. We
present TreeDST, a dataset of 27k conversations annotated with tree-structured
dialog states and system acts. We describe an encoder-decoder framework for DST
with hierarchical representations, which leads to 20% improvement over
state-of-the-art DST approaches that operate on a flat meaning space of
slot-value pairs.
| 2,021 |
Computation and Language
|
On Learning Text Style Transfer with Direct Rewards
|
In most cases, the lack of parallel corpora makes it impossible to directly
train supervised models for the text style transfer task. In this paper, we
explore training algorithms that instead optimize reward functions that
explicitly consider different aspects of the style-transferred outputs. In
particular, we leverage semantic similarity metrics originally used for
fine-tuning neural machine translation models to explicitly assess the
preservation of content between system outputs and input texts. We also
investigate the potential weaknesses of the existing automatic metrics and
propose efficient strategies of using these metrics for training. The
experimental results show that our model provides significant gains in both
automatic and human evaluation over strong baselines, indicating the
effectiveness of our proposed methods and training strategies.
| 2,021 |
Computation and Language
|
Structure-Grounded Pretraining for Text-to-SQL
|
Learning to capture text-table alignment is essential for tasks like
text-to-SQL. A model needs to correctly recognize natural language references
to columns and values and to ground them in the given database schema. In this
paper, we present a novel weakly supervised Structure-Grounded pretraining
framework (StruG) for text-to-SQL that can effectively learn to capture
text-table alignment based on a parallel text-table corpus. We identify a set
of novel prediction tasks: column grounding, value grounding and column-value
mapping, and leverage them to pretrain a text-table encoder. Additionally, to
evaluate different methods under more realistic text-table alignment settings,
we create a new evaluation set Spider-Realistic based on Spider dev set with
explicit mentions of column names removed, and adopt eight existing text-to-SQL
datasets for cross-database evaluation. STRUG brings significant improvement
over BERT-LARGE in all settings. Compared with existing pretraining methods
such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms
all baselines on more realistic sets. The Spider-Realistic dataset is available
at https://doi.org/10.5281/zenodo.5205322.
| 2,022 |
Computation and Language
|
Improved Synthetic Training for Reading Comprehension
|
Automatically generated synthetic training examples have been shown to
improve performance in machine reading comprehension (MRC). Compared to human
annotated gold standard data, synthetic training data has unique properties,
such as high availability at the possible expense of quality. In view of such
differences, in this paper, we explore novel applications of synthetic examples
to MRC. Our proposed pre-training and knowledge distillation strategies show
significant improvements over existing methods. In a particularly surprising
discovery, we observe that synthetic distillation often yields students that
can outperform the teacher model.
| 2,020 |
Computation and Language
|
Improving Multilingual Models with Language-Clustered Vocabularies
|
State-of-the-art multilingual models depend on vocabularies that cover all of
the languages the model will expect to see at inference time, but the standard
methods for generating those vocabularies are not ideal for massively
multilingual applications. In this work, we introduce a novel procedure for
multilingual vocabulary generation that combines the separately trained
vocabularies of several automatically derived language clusters, thus balancing
the trade-off between cross-lingual subword sharing and language-specific
vocabularies. Our experiments show improvements across languages on key
multilingual benchmark tasks TyDi QA (+2.9 F1), XNLI (+2.1\%), and WikiAnn NER
(+2.8 F1) and factor of 8 reduction in out-of-vocabulary rate, all without
increasing the size of the model or data.
| 2,020 |
Computation and Language
|
Fair Hate Speech Detection through Evaluation of Social Group
Counterfactuals
|
Approaches for mitigating bias in supervised models are designed to reduce
models' dependence on specific sensitive features of the input data, e.g.,
mentioned social groups. However, in the case of hate speech detection, it is
not always desirable to equalize the effects of social groups because of their
essential role in distinguishing outgroup-derogatory hate, such that particular
types of hateful rhetoric carry the intended meaning only when contextualized
around certain social group tokens. Counterfactual token fairness for a
mentioned social group evaluates the model's predictions as to whether they are
the same for (a) the actual sentence and (b) a counterfactual instance, which
is generated by changing the mentioned social group in the sentence. Our
approach assures robust model predictions for counterfactuals that imply
similar meaning as the actual sentence. To quantify the similarity of a
sentence and its counterfactual, we compare their likelihood score calculated
by generative language models. By equalizing model behaviors on each sentence
and its counterfactuals, we mitigate bias in the proposed model while
preserving the overall classification performance.
| 2,020 |
Computation and Language
|
Open-Domain Dialogue Generation Based on Pre-trained Language Models
|
Pre-trained language models have been successfully used in response
generation for open-domain dialogue. Four main frameworks have been proposed:
(1) Transformer-ED using Transformer encoder and decoder separately for source
and target sentences; (2) Transformer-Dec using Transformer decoder for both
source and target sentences; (3) Transformer-MLM using Transformer decoder that
applies bi-directional attention on the source side and left-to-right attention
on the target side with masked language model objective; and (4) Transformer-AR
that uses auto-regressive objective instead. In this study, we compare these
frameworks on 3 datasets, and our comparison reveals that the best framework
uses bidirectional attention on the source side and does not separate encoder
and decoder. We also examine model discrepancy, and our experiments confirm
that the performance of a model is directly impacted by the underlying
discrepancies. We then propose two correction methods to reduce the
discrepancies, and both improve the model performance. These results show that
discrepancies is an important factor to consider when we use a pre-trained
model, and a reduction in discrepancies can lead to improved performance.
| 2,020 |
Computation and Language
|
Deep Clustering of Text Representations for Supervision-free Probing of
Syntax
|
We explore deep clustering of text representations for unsupervised model
interpretation and induction of syntax. As these representations are
high-dimensional, out-of-the-box methods like KMeans do not work well. Thus,
our approach jointly transforms the representations into a lower-dimensional
cluster-friendly space and clusters them. We consider two notions of syntax:
Part of speech Induction (POSI) and constituency labelling (CoLab) in this
work. Interestingly, we find that Multilingual BERT (mBERT) contains surprising
amount of syntactic knowledge of English; possibly even as much as English BERT
(EBERT). Our model can be used as a supervision-free probe which is arguably a
less-biased way of probing. We find that unsupervised probes show benefits from
higher layers as compared to supervised probes. We further note that our
unsupervised probe utilizes EBERT and mBERT representations differently,
especially for POSI. We validate the efficacy of our probe by demonstrating its
capabilities as an unsupervised syntax induction technique. Our probe works
well for both syntactic formalisms by simply adapting the input
representations. We report competitive performance of our probe on 45-tag
English POSI, state-of-the-art performance on 12-tag POSI across 10 languages,
and competitive results on CoLab. We also perform zero-shot syntax induction on
resource impoverished languages and report strong results.
| 2,021 |
Computation and Language
|
Measuring the `I don't know' Problem through the Lens of Gricean
Quantity
|
We consider the intrinsic evaluation of neural generative dialog models
through the lens of Grice's Maxims of Conversation (1975). Based on the maxim
of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to
diagnose the `I don't know' problem, in which a dialog system produces generic
responses. The linguistically motivated RUQ diagnostic compares the model score
of a generic response to that of the reference response. We find that for
reasonable baseline models, `I don't know' is preferred over the reference the
majority of the time, but this can be reduced to less than 5% with
hyperparameter tuning. RUQ allows for the direct analysis of the `I don't know'
problem, which has been addressed but not analyzed by prior work.
| 2,021 |
Computation and Language
|
Document-level Event Extraction with Efficient End-to-end Learning of
Cross-event Dependencies
|
Fully understanding narratives often requires identifying events in the
context of whole documents and modeling the event relations. However,
document-level event extraction is a challenging task as it requires the
extraction of event and entity coreference, and capturing arguments that span
across different sentences. Existing works on event extraction usually confine
on extracting events from single sentences, which fail to capture the
relationships between the event mentions at the scale of a document, as well as
the event arguments that appear in a different sentence than the event trigger.
In this paper, we propose an end-to-end model leveraging Deep Value Networks
(DVN), a structured prediction algorithm, to efficiently capture cross-event
dependencies for document-level event extraction. Experimental results show
that our approach achieves comparable performance to CRF-based models on ACE05,
while enjoys significantly higher computational efficiency.
| 2,021 |
Computation and Language
|
New Approaches for Natural Language Understanding based on the Idea that
Natural Language encodes both Information and its Processing Procedures
|
We must recognize that natural language is a way of information encoding, and
it encodes not only the information but also the procedures for how information
is processed. To understand natural language, the same as we conceive and
design computer languages, the first step is to separate information (or data)
and the processing procedures of information (or data). In natural language,
some processing procedures of data are encoded directly as the structure chunk
and the pointer chunk (this paper has reclassified lexical chunks as the data
chunk, structure chunk, and the pointer chunk); some processing procedures of
data imply in sentences structures; some requests of processing procedures are
expressed by information senders and processed by information receivers. For
the data parts, the classification encoding system of attribute information and
the information organization architecture (including constitutional structures
of information sets and the hierarchy between the information sets) were
discussed. In section 2, the theoretical part elaborated in section 2 has been
verified in examples and proofed that the studies in this paper have achieved
the goal of enabling machines to understand the information conveyed in the
dialogue. In section 4, the author summarizes the basic conditions of
"Understanding", rethinks what "Understanding" is and how to proceed. The study
in this paper provides a practical, theoretical basis and research methods for
NLU. It also can be applied in large-scale and multi-type information
processing in the artificial intelligence (AI) area.
| 2,021 |
Computation and Language
|
X-Class: Text Classification with Extremely Weak Supervision
|
In this paper, we explore text classification with extremely weak
supervision, i.e., only relying on the surface text of class names. This is a
more challenging setting than the seed-driven weak supervision, which allows a
few seed words per class. We opt to attack this problem from a representation
learning perspective -- ideal document representations should lead to nearly
the same results between clustering and the desired classification. In
particular, one can classify the same corpus differently (e.g., based on topics
and locations), so document representations should be adaptive to the given
class names. We propose a novel framework X-Class to realize the adaptive
representations. Specifically, we first estimate class representations by
incrementally adding the most similar word to each class until inconsistency
arises. Following a tailored mixture of class attention mechanisms, we obtain
the document representation via a weighted average of contextualized word
representations. With the prior of each document assigned to its nearest class,
we then cluster and align the documents to classes. Finally, we pick the most
confident documents from each cluster to train a text classifier. Extensive
experiments demonstrate that X-Class can rival and even outperform seed-driven
weakly supervised methods on 7 benchmark datasets. Our dataset and code are
released at https://github.com/ZihanWangKi/XClass/ .
| 2,022 |
Computation and Language
|
CaM-Gen:Causally-aware Metric-guided Text Generation
|
Content is created for a well-defined purpose, often described by a metric or
signal represented in the form of structured information. The relationship
between the goal (metrics) of target content and the content itself is
non-trivial. While large-scale language models show promising text generation
capabilities, guiding the generated text with external metrics is challenging.
These metrics and content tend to have inherent relationships and not all of
them may be of consequence. We introduce CaM-Gen: Causally aware Generative
Networks guided by user-defined target metrics incorporating the causal
relationships between the metric and content features. We leverage causal
inference techniques to identify causally significant aspects of a text that
lead to the target metric and then explicitly guide generative models towards
these by a feedback mechanism. We propose this mechanism for variational
autoencoder and Transformer-based generative models. The proposed models beat
baselines in terms of the target metric control while maintaining fluency and
language quality of the generated text. To the best of our knowledge, this is
one of the early attempts at controlled generation incorporating a metric guide
using causal inference.
| 2,022 |
Computation and Language
|
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
|
We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval.
Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank,
Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from
55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query
Bank and Relevance Set, where the former contains 1,236 human-paraphrased
queries while the latter contains ~32 human-annotated FAQ items for each query.
We analyze COUGH by testing different FAQ retrieval models built on top of BM25
and BERT, among which the best model achieves 48.8 under P@5, indicating a
great challenge presented by COUGH and encouraging future research for further
improvement. Our COUGH dataset is available at
https://github.com/sunlab-osu/covid-faq.
| 2,021 |
Computation and Language
|
Pairwise Representation Learning for Event Coreference
|
Natural Language Processing tasks such as resolving the coreference of events
require understanding the relations between two text snippets. These tasks are
typically formulated as (binary) classification problems over independently
induced representations of the text snippets. In this work, we develop a
Pairwise Representation Learning (PairwiseRL) scheme for the event mention
pairs, in which we jointly encode a pair of text snippets so that the
representation of each mention in the pair is induced in the context of the
other one. Furthermore, our representation supports a finer, structured
representation of the text snippet to facilitate encoding events and their
arguments. We show that PairwiseRL, despite its simplicity, outperforms the
prior state-of-the-art event coreference systems on both cross-document and
within-document event coreference benchmarks. We also conduct in-depth analysis
in terms of the improvement and the limitation of pairwise representation so as
to provide insights for future work.
| 2,023 |
Computation and Language
|
A Frustratingly Easy Approach for Entity and Relation Extraction
|
End-to-end relation extraction aims to identify named entities and extract
relations between them. Most recent work models these two subtasks jointly,
either by casting them in one structured prediction framework, or performing
multi-task learning through shared representations. In this work, we present a
simple pipelined approach for entity and relation extraction, and establish the
new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC),
obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint
models with the same pre-trained encoders. Our approach essentially builds on
two independent encoders and merely uses the entity model to construct the
input for the relation model. Through a series of careful examinations, we
validate the importance of learning distinct contextual representations for
entities and relations, fusing entity information early in the relation model,
and incorporating global context. Finally, we also present an efficient
approximation to our approach which requires only one pass of both entity and
relation encoders at inference time, achieving an 8-16$\times$ speedup with a
slight reduction in accuracy.
| 2,021 |
Computation and Language
|
Constructing Taxonomies from Pretrained Language Models
|
We present a method for constructing taxonomic trees (e.g., WordNet) using
pretrained language models. Our approach is composed of two modules, one that
predicts parenthood relations and another that reconciles those predictions
into trees. The parenthood prediction module produces likelihood scores for
each potential parent-child pair, creating a graph of parent-child relation
scores. The tree reconciliation module treats the task as a graph optimization
problem and outputs the maximum spanning tree of this graph. We train our model
on subtrees sampled from WordNet, and test on non-overlapping WordNet subtrees.
We show that incorporating web-retrieved glosses can further improve
performance. On the task of constructing subtrees of English WordNet, the model
achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best
published result on this task. In addition, we convert the original English
dataset into nine other languages using Open Multilingual WordNet and extend
our results across these languages.
| 2,021 |
Computation and Language
|
"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses
|
Ad hominem attacks are those that target some feature of a person's character
instead of the position the person is maintaining. These attacks are harmful
because they propagate implicit biases and diminish a person's credibility.
Since dialogue systems respond directly to user input, it is important to study
ad hominems in dialogue responses. To this end, we propose categories of ad
hominems, compose an annotated dataset, and build a classifier to analyze human
and dialogue system responses to English Twitter posts. We specifically compare
responses to Twitter topics about marginalized communities (#BlackLivesMatter,
#MeToo) versus other topics (#Vegan, #WFH), because the abusive language of ad
hominems could further amplify the skew of power away from marginalized
populations. Furthermore, we propose a constrained decoding technique that uses
salient $n$-gram similarity as a soft constraint for top-$k$ sampling to reduce
the amount of ad hominems generated. Our results indicate that 1) responses
from both humans and DialoGPT contain more ad hominems for discussions around
marginalized communities, 2) different quantities of ad hominems in the
training data can influence the likelihood of generating ad hominems, and 3) we
can use constrained decoding techniques to reduce ad hominems in generated
dialogue responses.
| 2,021 |
Computation and Language
|
Rethinking embedding coupling in pre-trained language models
|
We re-evaluate the standard practice of sharing weights between input and
output embeddings in state-of-the-art pre-trained language models. We show that
decoupled embeddings provide increased modeling flexibility, allowing us to
significantly improve the efficiency of parameter allocation in the input
embedding of multilingual models. By reallocating the input embedding
parameters in the Transformer layers, we achieve dramatically better
performance on standard natural language understanding tasks with the same
number of parameters during fine-tuning. We also show that allocating
additional capacity to the output embedding provides benefits to the model that
persist through the fine-tuning stage even though the output embedding is
discarded after pre-training. Our analysis shows that larger output embeddings
prevent the model's last layers from overspecializing to the pre-training task
and encourage Transformer representations to be more general and more
transferable to other tasks and languages. Harnessing these findings, we are
able to train models that achieve strong performance on the XTREME benchmark
without increasing the number of parameters at the fine-tuning stage.
| 2,020 |
Computation and Language
|
Cross-neutralising: Probing for joint encoding of linguistic information
in multilingual models
|
Multilingual sentence encoders are widely used to transfer NLP models across
languages. The success of this transfer is, however, dependent on the model's
ability to encode the patterns of cross-lingual similarity and variation. Yet,
little is known as to how these models are able to do this. We propose a simple
method to study how relationships between languages are encoded in two
state-of-the-art multilingual models (i.e. M-BERT and XLM-R). The results
provide insight into their information sharing mechanisms and suggest that
linguistic properties are encoded jointly across typologically-similar
languages in these models.
| 2,021 |
Computation and Language
|
Text Editing by Command
|
A prevailing paradigm in neural text generation is one-shot generation, where
text is produced in a single step. The one-shot setting is inadequate, however,
when the constraints the user wishes to impose on the generated text are
dynamic, especially when authoring longer documents. We address this limitation
with an interactive text generation setting in which the user interacts with
the system by issuing commands to edit existing text. To this end, we propose a
novel text editing task, and introduce WikiDocEdits, a dataset of
single-sentence edits crawled from Wikipedia. We show that our Interactive
Editor, a transformer-based model trained on this dataset, outperforms
baselines and obtains positive results in both automatic and human evaluations.
We present empirical and qualitative analyses of this model's performance.
| 2,020 |
Computation and Language
|
Context-aware Decoder for Neural Machine Translation using a Target-side
Document-Level Language Model
|
Although many context-aware neural machine translation models have been
proposed to incorporate contexts in translation, most of those models are
trained end-to-end on parallel documents aligned in sentence-level. Because
only a few domains (and language pairs) have such document-level parallel data,
we cannot perform accurate context-aware translation in most domains. We
therefore present a simple method to turn a sentence-level translation model
into a context-aware model by incorporating a document-level language model
into the decoder. Our context-aware decoder is built upon only a sentence-level
parallel corpora and monolingual corpora; thus no document-level parallel data
is needed. In a theoretical viewpoint, the core part of this work is the novel
representation of contextual information using point-wise mutual information
between context and the current sentence. We show the effectiveness of our
approach in three language pairs, English to French, English to Russian, and
Japanese to English, by evaluation in \textsc{bleu} and contrastive tests for
context-aware translation.
| 2,021 |
Computation and Language
|
Keyphrase Extraction with Dynamic Graph Convolutional Networks and
Diversified Inference
|
Keyphrase extraction (KE) aims to summarize a set of phrases that accurately
express a concept or a topic covered in a given document. Recently,
Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE
task, and it has obtained competitive performance on various benchmarks. The
main challenges of Seq2Seq methods lie in acquiring informative latent document
representation and better modeling the compositionality of the target
keyphrases set, which will directly affect the quality of generated keyphrases.
In this paper, we propose to adopt the Dynamic Graph Convolutional Networks
(DGCN) to solve the above two problems simultaneously. Concretely, we explore
to integrate dependency trees with GCN for latent representation learning.
Moreover, the graph structure in our model is dynamically modified during the
learning process according to the generated keyphrases. To this end, our
approach is able to explicitly learn the relations within the keyphrases
collection and guarantee the information interchange between encoder and
decoder in both directions. Extensive experiments on various KE benchmark
datasets demonstrate the effectiveness of our approach.
| 2,020 |
Computation and Language
|
Multilingual Speech Translation with Efficient Finetuning of Pretrained
Models
|
We present a simple yet effective approach to build multilingual
speech-to-text (ST) translation by efficient transfer learning from pretrained
speech encoder and text decoder. Our key finding is that a minimalistic LNA
(LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and
cross-modality transfer ability by only finetuning less than 10% of the
pretrained parameters. This enables effectively leveraging large pretrained
models with low training cost. Using wav2vec 2.0 for acoustic modeling, and
mBART for multilingual text generation, our approach advanced the new
state-of-the-art for 34 translation directions (and surpassing cascaded ST for
23 of them) on large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on
average across 15 En-X directions and +5.1 BLEU on average across 19 X-En
directions). Our approach demonstrates strong zero-shot performance in a
many-to-many multilingual model (+5.7 BLEU on average across 18 non-English
directions), making it an appealing approach for attaining high-quality speech
translation with improved parameter and data efficiency.
| 2,021 |
Computation and Language
|
Unsupervised Vision-and-Language Pre-training Without Parallel Images
and Captions
|
Pre-trained contextual vision-and-language (V&L) models have achieved
impressive performance on various benchmarks. However, existing models require
a large amount of parallel image-caption data for pre-training. Such data are
costly to collect and require cumbersome curation. Inspired by unsupervised
machine translation, we investigate if a strong V&L representation model can be
learned through unsupervised pre-training without image-caption corpora. In
particular, we propose to conduct ``mask-and-predict'' pre-training on
text-only and image-only corpora and introduce the object tags detected by an
object recognition model as anchor points to bridge two modalities. We find
that such a simple approach achieves performance close to a model pre-trained
with aligned data, on four English V&L benchmarks. Our work challenges the
widely held notion that aligned data is necessary for V&L pre-training, while
significantly reducing the amount of supervision needed for V&L models.
| 2,021 |
Computation and Language
|
GO FIGURE: A Meta Evaluation of Factuality in Summarization
|
While neural language models can generate text with remarkable fluency and
coherence, controlling for factual correctness in generation remains an open
research question. This major discrepancy between the surface-level fluency and
the content-level correctness of neural generation has motivated a new line of
research that seeks automatic metrics for evaluating the factuality of machine
text. In this paper, we introduce GO FIGURE, a meta-evaluation framework for
evaluating factuality evaluation metrics. We propose five necessary and
intuitive conditions to evaluate factuality metrics on diagnostic factuality
data across three different summarization tasks. Our benchmark analysis on ten
factuality metrics reveals that our meta-evaluation framework provides a robust
and efficient evaluation that is extensible to multiple types of factual
consistency and standard generation metrics, including QA metrics. It also
reveals that while QA metrics generally improve over standard metrics that
measure factuality across domains, performance is highly dependent on the way
in which questions are generated.
| 2,021 |
Computation and Language
|
Improving Zero and Few-Shot Abstractive Summarization with Intermediate
Fine-tuning and Data Augmentation
|
Models pretrained with self-supervised objectives on large text corpora
achieve state-of-the-art performance on English text summarization tasks.
However, these models are typically fine-tuned on hundreds of thousands of data
points, an infeasible requirement when applying summarization to new, niche
domains. In this work, we introduce a novel and generalizable method, called
WikiTransfer, for fine-tuning pretrained models for summarization in an
unsupervised, dataset-specific manner. WikiTransfer fine-tunes pretrained
models on pseudo-summaries, produced from generic Wikipedia data, which contain
characteristics of the target dataset, such as the length and level of
abstraction of the desired summaries. WikiTransfer models achieve
state-of-the-art, zero-shot abstractive summarization performance on the
CNN-DailyMail dataset and demonstrate the effectiveness of our approach on
three additional diverse datasets. These models are more robust to noisy data
and also achieve better or comparable few-shot performance using 10 and 100
training examples when compared to few-shot transfer from other summarization
datasets. To further boost performance, we employ data augmentation via
round-trip translation as well as introduce a regularization term for improved
few-shot transfer. To understand the role of dataset aspects in transfer
performance and the quality of the resulting output summaries, we further study
the effect of the components of our unsupervised fine-tuning data and analyze
few-shot performance using both automatic and human evaluation.
| 2,021 |
Computation and Language
|
FLIN: A Flexible Natural Language Interface for Web Navigation
|
AI assistants can now carry out tasks for users by directly interacting with
website UIs. Current semantic parsing and slot-filling techniques cannot
flexibly adapt to many different websites without being constantly re-trained.
We propose FLIN, a natural language interface for web navigation that maps user
commands to concept-level actions (rather than low-level UI actions), thus
being able to flexibly adapt to different websites and handle their transient
nature. We frame this as a ranking problem: given a user command and a webpage,
FLIN learns to score the most relevant navigation instruction (involving action
and parameter values). To train and evaluate FLIN, we collect a dataset using
nine popular websites from three domains. Our results show that FLIN was able
to adapt to new websites in a given domain.
| 2,021 |
Computation and Language
|
CoCo: Controllable Counterfactuals for Evaluating Dialogue State
Trackers
|
Dialogue state trackers have made significant progress on benchmark datasets,
but their generalization capability to novel and realistic scenarios beyond the
held-out conversations is less understood. We propose controllable
counterfactuals (CoCo) to bridge this gap and evaluate dialogue state tracking
(DST) models on novel scenarios, i.e., would the system successfully tackle the
request if the user responded differently but still consistently with the
dialogue flow? CoCo leverages turn-level belief states as counterfactual
conditionals to produce novel conversation scenarios in two steps: (i)
counterfactual goal generation at turn-level by dropping and adding slots
followed by replacing slot values, (ii) counterfactual conversation generation
that is conditioned on (i) and consistent with the dialogue flow. Evaluating
state-of-the-art DST models on MultiWOZ dataset with CoCo-generated
counterfactuals results in a significant performance drop of up to 30.8% (from
49.4% to 18.6%) in absolute joint goal accuracy. In comparison, widely used
techniques like paraphrasing only affect the accuracy by at most 2%. Human
evaluations show that COCO-generated conversations perfectly reflect the
underlying user goal with more than 95% accuracy and are as human-like as the
original conversations, further strengthening its reliability and promise to be
adopted as part of the robustness evaluation of DST models.
| 2,021 |
Computation and Language
|
ReadOnce Transformers: Reusable Representations of Text for Transformers
|
We present ReadOnce Transformers, an approach to convert a transformer-based
model into one that can build an information-capturing, task-independent, and
compressed representation of text. The resulting representation is reusable
across different examples and tasks, thereby requiring a document shared across
many examples or tasks to only be \emph{read once}. This leads to faster
training and evaluation of models. Additionally, we extend standard
text-to-text transformer models to Representation+Text-to-text models, and
evaluate on multiple downstream tasks: multi-hop QA, abstractive QA, and
long-document summarization. Our one-time computed representation results in a
2x-5x speedup compared to standard text-to-text models, while the compression
also allows existing language models to handle longer documents without the
need for designing new pre-trained models.
| 2,021 |
Computation and Language
|
When Being Unseen from mBERT is just the Beginning: Handling New
Languages With Multilingual Language Models
|
Transfer learning based on pretraining language models on a large amount of
raw data has become a new norm to reach state-of-the-art performance in NLP.
Still, it remains unclear how this approach should be applied for unseen
languages that are not covered by any available large-scale multilingual
language model and for which only a small amount of raw data is generally
available. In this work, by comparing multilingual and monolingual models, we
show that such models behave in multiple ways on unseen languages. Some
languages greatly benefit from transfer learning and behave similarly to
closely related high resource languages whereas others apparently do not.
Focusing on the latter, we show that this failure to transfer is largely
related to the impact of the script used to write such languages.
Transliterating those languages improves very significantly the ability of
large-scale multilingual language models on downstream tasks.
| 2,021 |
Computation and Language
|
On Transferability of Bias Mitigation Effects in Language Model
Fine-Tuning
|
Fine-tuned language models have been shown to exhibit biases against
protected groups in a host of modeling tasks such as text classification and
coreference resolution. Previous works focus on detecting these biases,
reducing bias in data representations, and using auxiliary training objectives
to mitigate bias during fine-tuning. Although these techniques achieve bias
reduction for the task and domain at hand, the effects of bias mitigation may
not directly transfer to new tasks, requiring additional data collection and
customized annotation of sensitive attributes, and re-evaluation of appropriate
fairness metrics. We explore the feasibility and benefits of upstream bias
mitigation (UBM) for reducing bias on downstream tasks, by first applying bias
mitigation to an upstream model through fine-tuning and subsequently using it
for downstream fine-tuning. We find, in extensive experiments across hate
speech detection, toxicity detection, occupation prediction, and coreference
resolution tasks over various bias factors, that the effects of UBM are indeed
transferable to new downstream tasks or domains via fine-tuning, creating less
biased downstream models than directly fine-tuning on the downstream task or
transferring from a vanilla upstream model. Though challenges remain, we show
that UBM promises more efficient and accessible bias mitigation in LM
fine-tuning.
| 2,021 |
Computation and Language
|
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine
Translation
|
Non-Autoregressive machine Translation (NAT) models have demonstrated
significant inference speedup but suffer from inferior translation accuracy.
The common practice to tackle the problem is transferring the Autoregressive
machine Translation (AT) knowledge to NAT models, e.g., with knowledge
distillation. In this work, we hypothesize and empirically verify that AT and
NAT encoders capture different linguistic properties of source sentences.
Therefore, we propose to adopt Multi-Task learning to transfer the AT knowledge
to NAT models through encoder sharing. Specifically, we take the AT model as an
auxiliary task to enhance NAT model performance. Experimental results on WMT14
English-German and WMT16 English-Romanian datasets show that the proposed
Multi-Task NAT achieves significant improvements over the baseline NAT models.
Furthermore, the performance on large-scale WMT19 and WMT20 English-German
datasets confirm the consistency of our proposed method. In addition,
experimental results demonstrate that our Multi-Task NAT is complementary to
knowledge distillation, the standard knowledge transfer method for NAT.
| 2,021 |
Computation and Language
|
Large Scale Legal Text Classification Using Transformer Models
|
Large multi-label text classification is a challenging Natural Language
Processing (NLP) problem that is concerned with text classification for
datasets with thousands of labels. We tackle this problem in the legal domain,
where datasets, such as JRC-Acquis and EURLEX57K labeled with the EuroVoc
vocabulary were created within the legal information systems of the European
Union. The EuroVoc taxonomy includes around 7000 concepts. In this work, we
study the performance of various recent transformer-based models in combination
with strategies such as generative pretraining, gradual unfreezing and
discriminative learning rates in order to reach competitive classification
performance, and present new state-of-the-art results of 0.661 (F1) for
JRC-Acquis and 0.754 for EURLEX57K. Furthermore, we quantify the impact of
individual steps, such as language model fine-tuning or gradual unfreezing in
an ablation study, and provide reference dataset splits created with an
iterative stratification algorithm.
| 2,020 |
Computation and Language
|
Learning to Deceive Knowledge Graph Augmented Models via Targeted
Perturbation
|
Knowledge graphs (KGs) have helped neural models improve performance on
various knowledge-intensive tasks, like question answering and item
recommendation. By using attention over the KG, such KG-augmented models can
also "explain" which KG information was most relevant for making a given
prediction. In this paper, we question whether these models are really behaving
as we expect. We show that, through a reinforcement learning policy (or even
simple heuristics), one can produce deceptively perturbed KGs, which maintain
the downstream performance of the original KG while significantly deviating
from the original KG's semantics and structure. Our findings raise doubts about
KG-augmented models' ability to reason about KG information and give sensible
explanations.
| 2,021 |
Computation and Language
|
Learning Contextualized Knowledge Structures for Commonsense Reasoning
|
Recently, knowledge graph (KG) augmented models have achieved noteworthy
success on various commonsense reasoning tasks. However, KG edge (fact)
sparsity and noisy edge extraction/generation often hinder models from
obtaining useful knowledge to reason over. To address these issues, we propose
a new KG-augmented model: Hybrid Graph Network (HGN). Unlike prior methods, HGN
learns to jointly contextualize extracted and generated knowledge by reasoning
over both within a unified graph structure. Given the task input context and an
extracted KG subgraph, HGN is trained to generate embeddings for the subgraph's
missing edges to form a "hybrid" graph, then reason over the hybrid graph while
filtering out context-irrelevant edges. We demonstrate HGN's effectiveness
through considerable performance gains across four commonsense reasoning
benchmarks, plus a user study on edge validness and helpfulness.
| 2,021 |
Computation and Language
|
Revisiting Neural Language Modelling with Syllables
|
Language modelling is regularly analysed at word, subword or character units,
but syllables are seldom used. Syllables provide shorter sequences than
characters, they can be extracted with rules, and their segmentation typically
requires less specialised effort than identifying morphemes. We reconsider
syllables for an open-vocabulary generation task in 20 languages. We use
rule-based syllabification methods for five languages and address the rest with
a hyphenation tool, which behaviour as syllable proxy is validated. With a
comparable perplexity, we show that syllables outperform characters, annotated
morphemes and unsupervised subwords. Finally, we also study the overlapping of
syllables concerning other subword pieces and discuss some limitations and
opportunities.
| 2,020 |
Computation and Language
|
FedE: Embedding Knowledge Graphs in Federated Setting
|
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's
important to do Knowledge Graph Completion (KGC) by predicting missing triples.
Multi-Source KG is a common situation in real KG applications which can be
viewed as a set of related individual KGs where different KGs contains
relations of different aspects of entities. It's intuitive that, for each
individual KG, its completion could be greatly contributed by the triples
defined and labeled in other ones. However, because of the data privacy and
sensitivity, a set of relevant knowledge graphs cannot complement each other's
KGC by just collecting data from different knowledge graphs together.
Therefore, in this paper, we introduce federated setting to keep their privacy
without triple transferring between KGs and apply it in embedding knowledge
graph, a typical method which have proven effective for KGC in the past decade.
We propose a Federated Knowledge Graph Embedding framework FedE, focusing on
learning knowledge graph embeddings by aggregating locally-computed updates.
Finally, we conduct extensive experiments on datasets derived from KGE
benchmark datasets and results show the effectiveness of our proposed FedE.
| 2,020 |
Computation and Language
|
NeuroLogic Decoding: (Un)supervised Neural Text Generation with
Predicate Logic Constraints
|
Conditional text generation often requires lexical constraints, i.e., which
words should or shouldn't be included in the output text. While the dominant
recipe for conditional text generation has been large-scale pretrained language
models that are finetuned on the task-specific training data, such models do
not learn to follow the underlying constraints reliably, even when supervised
with large amounts of task-specific examples.
We propose NeuroLogic Decoding, a simple yet effective algorithm that enables
neural language models -- supervised or not -- to generate fluent text while
satisfying complex lexical constraints. Our approach is powerful yet efficient.
It handles any set of lexical constraints that is expressible under predicate
logic, while its asymptotic runtime is equivalent to conventional beam search.
Empirical results on four benchmarks show that NeuroLogic Decoding
outperforms previous approaches, including algorithms that handle a subset of
our constraints. Moreover, we find that unsupervised models with NeuroLogic
Decoding often outperform supervised models with conventional decoding, even
when the latter is based on considerably larger networks. Our results suggest
the limit of large-scale neural networks for fine-grained controllable
generation and the promise of inference-time algorithms.
| 2,021 |
Computation and Language
|
Unsupervised Paraphrasing with Pretrained Language Models
|
Paraphrase generation has benefited extensively from recent progress in the
designing of training objectives and model architectures. However, previous
explorations have largely focused on supervised methods, which require a large
amount of labeled data that is costly to collect. To address this drawback, we
adopt a transfer learning approach and propose a training pipeline that enables
pre-trained language models to generate high-quality paraphrases in an
unsupervised setting. Our recipe consists of task-adaptation, self-supervision,
and a novel decoding algorithm named Dynamic Blocking (DB). To enforce a
surface form dissimilar from the input, whenever the language model emits a
token contained in the source sequence, DB prevents the model from outputting
the subsequent source token for the next generation step. We show with
automatic and human evaluations that our approach achieves state-of-the-art
performance on both the Quora Question Pair (QQP) and the ParaNMT datasets and
is robust to domain shift between the two datasets of distinct distributions.
We also demonstrate that our model transfers to paraphrasing in other languages
without any additional finetuning.
| 2,021 |
Computation and Language
|
Word Embeddings for Chemical Patent Natural Language Processing
|
We evaluate chemical patent word embeddings against known biomedical
embeddings and show that they outperform the latter extrinsically and
intrinsically. We also show that using contextualized embeddings can induce
predictive models of reasonable performance for this domain over a relatively
small gold standard.
| 2,020 |
Computation and Language
|
Causal Effects of Linguistic Properties
|
We consider the problem of using observational data to estimate the causal
effects of linguistic properties. For example, does writing a complaint
politely lead to a faster response time? How much will a positive product
review increase sales? This paper addresses two technical challenges related to
the problem before developing a practical method. First, we formalize the
causal quantity of interest as the effect of a writer's intent, and establish
the assumptions necessary to identify this from observational data. Second, in
practice, we only have access to noisy proxies for the linguistic properties of
interest -- e.g., predictions from classifiers and lexicons. We propose an
estimator for this setting and prove that its bias is bounded when we perform
an adjustment for the text. Based on these results, we introduce TextCause, an
algorithm for estimating causal effects of linguistic properties. The method
leverages (1) distant supervision to improve the quality of noisy proxies, and
(2) a pre-trained language model (BERT) to adjust for the text. We show that
the proposed method outperforms related approaches when estimating the effect
of Amazon review sentiment on semi-simulated sales figures. Finally, we present
an applied case study investigating the effects of complaint politeness on
bureaucratic response times.
| 2,021 |
Computation and Language
|
Disease Normalization with Graph Embeddings
|
The detection and normalization of diseases in biomedical texts are key
biomedical natural language processing tasks. Disease names need not only be
identified, but also normalized or linked to clinical taxonomies describing
diseases such as MeSH. In this paper we describe deep learning methods that
tackle both tasks. We train and test our methods on the known NCBI disease
benchmark corpus. We propose to represent disease names by leveraging MeSH's
graphical structure together with the lexical information available in the
taxonomy using graph embeddings. We also show that combining neural named
entity recognition models with our graph-based entity linking methods via
multitask learning leads to improved disease recognition in the NCBI corpus.
| 2,020 |
Computation and Language
|
A Benchmark Corpus and Neural Approach for Sanskrit Derivative Nouns
Analysis
|
This paper presents first benchmark corpus of Sanskrit Pratyaya (suffix) and
inflectional words (padas) formed due to suffixes along with neural network
based approaches to process the formation and splitting of inflectional words.
Inflectional words spans the primary and secondary derivative nouns as the
scope of current work. Pratyayas are an important dimension of morphological
analysis of Sanskrit texts. There have been Sanskrit Computational Linguistics
tools for processing and analyzing Sanskrit texts. Unfortunately there has not
been any work to standardize & validate these tools specifically for derivative
nouns analysis. In this work, we prepared a Sanskrit suffix benchmark corpus
called Pratyaya-Kosh to evaluate the performance of tools. We also present our
own neural approach for derivative nouns analysis while evaluating the same on
most prominent Sanskrit Morphological Analysis tools. This benchmark will be
freely dedicated and available to researchers worldwide and we hope it will
motivate all to improve morphological analysis in Sanskrit Language.
| 2,020 |
Computation and Language
|
Neural Compound-Word (Sandhi) Generation and Splitting in Sanskrit
Language
|
This paper describes neural network based approaches to the process of the
formation and splitting of word-compounding, respectively known as the Sandhi
and Vichchhed, in Sanskrit language. Sandhi is an important idea essential to
morphological analysis of Sanskrit texts. Sandhi leads to word transformations
at word boundaries. The rules of Sandhi formation are well defined but complex,
sometimes optional and in some cases, require knowledge about the nature of the
words being compounded. Sandhi split or Vichchhed is an even more difficult
task given its non uniqueness and context dependence. In this work, we propose
the route of formulating the problem as a sequence to sequence prediction task,
using modern deep learning techniques. Being the first fully data driven
technique, we demonstrate that our model has an accuracy better than the
existing methods on multiple standard datasets, despite not using any
additional lexical or morphological resources. The code is being made available
at https://github.com/IITD-DataScience/Sandhi_Prakarana
| 2,020 |
Computation and Language
|
Unsupervised Learning of Disentangled Speech Content and Style
Representation
|
We present an approach for unsupervised learning of speech representation
disentangling contents and styles. Our model consists of: (1) a local encoder
that captures per-frame information; (2) a global encoder that captures
per-utterance information; and (3) a conditional decoder that reconstructs
speech given local and global latent variables. Our experiments show that (1)
the local latent variables encode speech contents, as reconstructed speech can
be recognized by ASR with low word error rates (WER), even with a different
global encoding; (2) the global latent variables encode speaker style, as
reconstructed speech shares speaker identity with the source utterance of the
global encoding. Additionally, we demonstrate an useful application from our
pre-trained model, where we can train a speaker recognition model from the
global latent variables and achieve high accuracy by fine-tuning with as few
data as one label per speaker.
| 2,021 |
Computation and Language
|
Pre-trained Summarization Distillation
|
Recent state-of-the-art approaches to summarization utilize large pre-trained
Transformer models. Distilling these models to smaller student models has
become critically important for practical use; however there are many different
distillation methods proposed by the NLP literature. Recent work on distilling
BERT for classification and regression tasks shows strong performance using
direct knowledge distillation. Alternatively, machine translation practitioners
distill using pseudo-labeling, where a small model is trained on the
translations of a larger model. A third, simpler approach is to 'shrink and
fine-tune' (SFT), which avoids any explicit distillation by copying parameters
to a smaller student model and then fine-tuning. We compare these three
approaches for distillation of Pegasus and BART, the current and former state
of the art, pre-trained summarization models, and find that SFT outperforms
knowledge distillation and pseudo-labeling on the CNN/DailyMail dataset, but
under-performs pseudo-labeling on the more abstractive XSUM dataset. PyTorch
Code and checkpoints of different sizes are available through Hugging Face
transformers here http://tiny.cc/4iy0tz.
| 2,020 |
Computation and Language
|
Discriminative Nearest Neighbor Few-Shot Intent Detection by
Transferring Natural Language Inference
|
Intent detection is one of the core components of goal-oriented dialog
systems, and detecting out-of-scope (OOS) intents is also a practically
important skill. Few-shot learning is attracting much attention to mitigate
data scarcity, but OOS detection becomes even more challenging. In this paper,
we present a simple yet effective approach, discriminative nearest neighbor
classification with deep self-attention. Unlike softmax classifiers, we
leverage BERT-style pairwise encoding to train a binary classifier that
estimates the best matched training example for a user input. We propose to
boost the discriminative ability by transferring a natural language inference
(NLI) model. Our extensive experiments on a large-scale multi-domain intent
detection task show that our method achieves more stable and accurate in-domain
and OOS detection accuracy than RoBERTa-based classifiers and embedding-based
nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot
model to perform competitively with 50-shot or even full-shot classifiers,
while we can keep the inference time constant by leveraging a faster embedding
retrieval model.
| 2,020 |
Computation and Language
|
CRAB: Class Representation Attentive BERT for Hate Speech Identification
in Social Media
|
In recent years, social media platforms have hosted an explosion of hate
speech and objectionable content. The urgent need for effective automatic hate
speech detection models have drawn remarkable investment from companies and
researchers. Social media posts are generally short and their semantics could
drastically be altered by even a single token. Thus, it is crucial for this
task to learn context-aware input representations, and consider relevancy
scores between input embeddings and class representations as an additional
signal. To accommodate these needs, this paper introduces CRAB (Class
Representation Attentive BERT), a neural model for detecting hate speech in
social media. The model benefits from two semantic representations: (i)
trainable token-wise and sentence-wise class representations, and (ii)
contextualized input embeddings from state-of-the-art BERT encoder. To
investigate effectiveness of CRAB, we train our model on Twitter data and
compare it against strong baselines. Our results show that CRAB achieves 1.89%
relative improved Macro-averaged F1 over state-of-the-art baseline. The results
of this research open an opportunity for the future research on automated
abusive behavior detection in social media
| 2,020 |
Computation and Language
|
Towards Medical Knowmetrics: Representing and Computing Medical
Knowledge using Semantic Predications as the Knowledge Unit and the
Uncertainty as the Knowledge Context
|
In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept
"knowledge unit" and "knowmetrics" for measuring knowledge. However, the
definition of "computable knowledge object" remains controversial so far in
different fields. For example, it is defined as 1) quantitative scientific
concept in natural science and engineering, 2) knowledge point in the field of
education research, and 3) semantic predications, i.e.,
Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic
MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples
extracted from medical literature, provides a basic data infrastructure for
measuring medical knowledge. In general, the study of extracting SPO triples as
computable knowledge unit from unstructured scientific text has been
overwhelmingly focusing on scientific knowledge per se. Since the SPO triples
would be possibly extracted from hypothetical, speculative statements or even
conflicting and contradictory assertions, the knowledge status (i.e., the
uncertainty), which serves as an integral and critical part of scientific
knowledge has been largely overlooked. This article aims to put forward a
framework for Medical Knowmetrics using the SPO triples as the knowledge unit
and the uncertainty as the knowledge context. The lung cancer publications
dataset is used to validate the proposed framework. The uncertainty of medical
knowledge and how its status evolves over time indirectly reflect the strength
of competing knowledge claims, and the probability of certainty for a given SPO
triple. We try to discuss the new insights using the uncertainty-centric
approaches to detect research fronts, and identify knowledge claims with high
certainty level, in order to improve the efficacy of knowledge-driven decision
support.
| 2,020 |
Computation and Language
|
Fine-tuning ERNIE for chest abnormal imaging signs extraction
|
Chest imaging reports describe the results of chest radiography procedures.
Automatic extraction of abnormal imaging signs from chest imaging reports has a
pivotal role in clinical research and a wide range of downstream medical tasks.
However, there are few studies on information extraction from Chinese chest
imaging reports. In this paper, we formulate chest abnormal imaging sign
extraction as a sequence tagging and matching problem. On this basis, we
propose a transferred abnormal imaging signs extractor with pretrained ERNIE as
the backbone, named EASON (fine-tuning ERNIE with CRF for Abnormal Signs
ExtractiON), which can address the problem of data insufficiency. In addition,
to assign the attributes (the body part and degree) to corresponding abnormal
imaging signs from the results of the sequence tagging model, we design a
simple but effective tag2relation algorithm based on the nature of chest
imaging report text. We evaluate our method on the corpus provided by a medical
big data company, and the experimental results demonstrate that our method
achieves significant and consistent improvement compared to other baselines.
| 2,020 |
Computation and Language
|
Orthros: Non-autoregressive End-to-end Speech Translation with
Dual-decoder
|
Fast inference speed is an important goal towards real-world deployment of
speech translation (ST) systems. End-to-end (E2E) models based on the
encoder-decoder architecture are more suitable for this goal than traditional
cascaded systems, but their effectiveness regarding decoding speed has not been
explored so far. Inspired by recent progress in non-autoregressive (NAR)
methods in text-based translation, which generates target tokens in parallel by
eliminating conditional dependencies, we study the problem of NAR decoding for
E2E-ST. We propose a novel NAR E2E-ST framework, Orthros, in which both NAR and
autoregressive (AR) decoders are jointly trained on the shared speech encoder.
The latter is used for selecting better translation among various length
candidates generated from the former, which dramatically improves the
effectiveness of a large length beam with negligible overhead. We further
investigate effective length prediction methods from speech inputs and the
impact of vocabulary sizes. Experiments on four benchmarks show the
effectiveness of the proposed method in improving inference speed while
maintaining competitive translation quality compared to state-of-the-art AR
E2E-ST systems.
| 2,021 |
Computation and Language
|
Commonsense knowledge adversarial dataset that challenges ELECTRA
|
Commonsense knowledge is critical in human reading comprehension. While
machine comprehension has made significant progress in recent years, the
ability in handling commonsense knowledge remains limited. Synonyms are one of
the most widely used commonsense knowledge. Constructing adversarial dataset is
an important approach to find weak points of machine comprehension models and
support the design of solutions. To investigate machine comprehension models'
ability in handling the commonsense knowledge, we created a Question and Answer
Dataset with common knowledge of Synonyms (QADS). QADS are questions generated
based on SQuAD 2.0 by applying commonsense knowledge of synonyms. The synonyms
are extracted from WordNet. Words often have multiple meanings and synonyms. We
used an enhanced Lesk algorithm to perform word sense disambiguation to
identify synonyms for the context. ELECTRA achieves the state-of-art result on
the SQuAD 2.0 dataset in 2019. With scale, ELECTRA can achieve similar
performance as BERT does. However, QADS shows that ELECTRA has little ability
to handle commonsense knowledge of synonyms. In our experiment, ELECTRA-small
can achieve 70% accuracy on SQuAD 2.0, but only 20% on QADS. ELECTRA-large did
not perform much better. Its accuracy on SQuAD 2.0 is 88% but dropped
significantly to 26% on QADS. In our earlier experiments, BERT, although also
failed badly on QADS, was not as bad as ELECTRA. The result shows that even
top-performing NLP models have little ability to handle commonsense knowledge
which is essential in reading comprehension.
| 2,020 |
Computation and Language
|
Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense
Knowledge
|
Understanding context-dependent variation in word meanings is a key aspect of
human language comprehension supported by the lexicon. Lexicographic resources
(e.g., WordNet) capture only some of this context-dependent variation; for
example, they often do not encode how closely senses, or discretized word
meanings, are related to one another. Our work investigates whether recent
advances in NLP, specifically contextualized word embeddings, capture
human-like distinctions between English word senses, such as polysemy and
homonymy. We collect data from a behavioral, web-based experiment, in which
participants provide judgments of the relatedness of multiple WordNet senses of
a word in a two-dimensional spatial arrangement task. We find that
participants' judgments of the relatedness between senses are correlated with
distances between senses in the BERT embedding space. Homonymous senses (e.g.,
bat as mammal vs. bat as sports equipment) are reliably more distant from one
another in the embedding space than polysemous ones (e.g., chicken as animal
vs. chicken as meat). Our findings point towards the potential utility of
continuous-space representations of sense meanings.
| 2,020 |
Computation and Language
|
Transgender Community Sentiment Analysis from Social Media Data: A
Natural Language Processing Approach
|
Transgender community is experiencing a huge disparity in mental health
conditions compared with the general population. Interpreting the social medial
data posted by transgender people may help us understand the sentiments of
these sexual minority groups better and apply early interventions. In this
study, we manually categorize 300 social media comments posted by transgender
people to the sentiment of negative, positive, and neutral. 5 machine learning
algorithms and 2 deep neural networks are adopted to build sentiment analysis
classifiers based on the annotated data. Results show that our annotations are
reliable with a high Cohen's Kappa score over 0.8 across all three classes.
LSTM model yields an optimal performance of accuracy over 0.85 and AUC of
0.876. Our next step will focus on using advanced natural language processing
algorithms on a larger annotated dataset.
| 2,022 |
Computation and Language
|
Autoencoding Improves Pre-trained Word Embeddings
|
Prior work investigating the geometry of pre-trained word embeddings have
shown that word embeddings to be distributed in a narrow cone and by centering
and projecting using principal component vectors one can increase the accuracy
of a given set of pre-trained word embeddings. However, theoretically, this
post-processing step is equivalent to applying a linear autoencoder to minimise
the squared l2 reconstruction error. This result contradicts prior work (Mu and
Viswanath, 2018) that proposed to remove the top principal components from
pre-trained embeddings. We experimentally verify our theoretical claims and
show that retaining the top principal components is indeed useful for improving
pre-trained word embeddings, without requiring access to additional linguistic
resources or labelled data.
| 2,020 |
Computation and Language
|
Two-stage Textual Knowledge Distillation for End-to-End Spoken Language
Understanding
|
End-to-end approaches open a new way for more accurate and efficient spoken
language understanding (SLU) systems by alleviating the drawbacks of
traditional pipeline systems. Previous works exploit textual information for an
SLU model via pre-training with automatic speech recognition or fine-tuning
with knowledge distillation. To utilize textual information more effectively,
this work proposes a two-stage textual knowledge distillation method that
matches utterance-level representations and predicted logits of two modalities
during pre-training and fine-tuning, sequentially. We use vq-wav2vec BERT as a
speech encoder because it captures general and rich features. Furthermore, we
improve the performance, especially in a low-resource scenario, with data
augmentation methods by randomly masking spans of discrete audio tokens and
contextualized hidden representations. Consequently, we push the
state-of-the-art on the Fluent Speech Commands, achieving 99.7% test accuracy
in the full dataset setting and 99.5% in the 10% subset setting. Throughout the
ablation studies, we empirically verify that all used methods are crucial to
the final performance, providing the best practice for spoken language
understanding. Code is available at https://github.com/clovaai/textual-kd-slu.
| 2,021 |
Computation and Language
|
Fair Embedding Engine: A Library for Analyzing and Mitigating Gender
Bias in Word Embeddings
|
Non-contextual word embedding models have been shown to inherit human-like
stereotypical biases of gender, race and religion from the training corpora. To
counter this issue, a large body of research has emerged which aims to mitigate
these biases while keeping the syntactic and semantic utility of embeddings
intact. This paper describes Fair Embedding Engine (FEE), a library for
analysing and mitigating gender bias in word embeddings. FEE combines various
state of the art techniques for quantifying, visualising and mitigating gender
bias in word embeddings under a standard abstraction. FEE will aid
practitioners in fast track analysis of existing debiasing methods on their
embedding models. Further, it will allow rapid prototyping of new methods by
evaluating their performance on a suite of standard metrics.
| 2,020 |
Computation and Language
|
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation
Shared Task
|
This paper describes the submission of LMU Munich to the WMT 2020
unsupervised shared task, in two language directions, German<->Upper Sorbian.
Our core unsupervised neural machine translation (UNMT) system follows the
strategy of Chronopoulou et al. (2020), using a monolingual pretrained language
generation model (on German) and fine-tuning it on both German and Upper
Sorbian, before initializing a UNMT model, which is trained with online
backtranslation. Pseudo-parallel data obtained from an unsupervised statistical
machine translation (USMT) system is used to fine-tune the UNMT model. We also
apply BPE-Dropout to the low resource (Upper Sorbian) data to obtain a more
robust system. We additionally experiment with residual adapters and find them
useful in the Upper Sorbian->German direction. We explore sampling during
backtranslation and curriculum learning to use SMT translations in a more
principled way. Finally, we ensemble our best-performing systems and reach a
BLEU score of 32.4 on German->Upper Sorbian and 35.2 on Upper Sorbian->German.
| 2,020 |
Computation and Language
|
LXPER Index 2.0: Improving Text Readability Assessment Model for L2
English Students in Korea
|
Developing a text readability assessment model specifically for texts in a
foreign English Language Training (ELT) curriculum has never had much attention
in the field of Natural Language Processing. Hence, most developed models show
extremely low accuracy for L2 English texts, up to the point where not many
even serve as a fair comparison. In this paper, we investigate a text
readability assessment model for L2 English learners in Korea. In accordance,
we improve and expand the Text Corpus of the Korean ELT curriculum
(CoKEC-text). Each text is labeled with its target grade level. We train our
model with CoKEC-text and significantly improve the accuracy of readability
assessment for texts in the Korean ELT curriculum.
| 2,020 |
Computation and Language
|
Introducing Syntactic Structures into Target Opinion Word Extraction
with Deep Learning
|
Targeted opinion word extraction (TOWE) is a sub-task of aspect based
sentiment analysis (ABSA) which aims to find the opinion words for a given
aspect-term in a sentence. Despite their success for TOWE, the current deep
learning models fail to exploit the syntactic information of the sentences that
have been proved to be useful for TOWE in the prior research. In this work, we
propose to incorporate the syntactic structures of the sentences into the deep
learning models for TOWE, leveraging the syntax-based opinion possibility
scores and the syntactic connections between the words. We also introduce a
novel regularization technique to improve the performance of the deep learning
models based on the representation distinctions between the words in TOWE. The
proposed model is extensively analyzed and achieves the state-of-the-art
performance on four benchmark datasets.
| 2,020 |
Computation and Language
|
FastFormers: Highly Efficient Transformer Models for Natural Language
Understanding
|
Transformer-based models are the state-of-the-art for Natural Language
Understanding (NLU) applications. Models are getting bigger and better on
various tasks. However, Transformer models remain computationally challenging
since they are not efficient at inference-time compared to traditional
approaches. In this paper, we present FastFormers, a set of recipes to achieve
efficient inference-time performance for Transformer-based models on various
NLU tasks. We show how carefully utilizing knowledge distillation, structured
pruning and numerical optimization can lead to drastic improvements on
inference efficiency. We provide effective recipes that can guide practitioners
to choose the best settings for various NLU tasks and pretrained models.
Applying the proposed recipes to the SuperGLUE benchmark, we achieve from 9.8x
up to 233.9x speed-up compared to out-of-the-box models on CPU. On GPU, we also
achieve up to 12.4x speed-up with the presented methods. We show that
FastFormers can drastically reduce cost of serving 100 million requests from
4,223 USD to just 18 USD on an Azure F16s_v2 instance. This translates to a
sustainable runtime by reducing energy consumption 6.9x - 125.8x according to
the metrics used in the SustaiNLP 2020 shared task.
| 2,020 |
Computation and Language
|
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional
Networks and Syntax-based Regulation
|
Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment
polarity of a sentence toward a specific aspect. Recently, it has been shown
that dependency trees can be integrated into deep learning models to produce
the state-of-the-art performance for ABSA. However, these models tend to
compute the hidden/representation vectors without considering the aspect terms
and fail to benefit from the overall contextual importance scores of the words
that can be obtained from the dependency tree for ABSA. In this work, we
propose a novel graph-based deep learning model to overcome these two issues of
the prior work on ABSA. In our model, gate vectors are generated from the
representation vectors of the aspect terms to customize the hidden vectors of
the graph-based models toward the aspect terms. In addition, we propose a
mechanism to obtain the importance scores for each word in the sentences based
on the dependency trees that are then injected into the model to improve the
representation vectors for ABSA. The proposed model achieves the
state-of-the-art performance on three benchmark datasets.
| 2,020 |
Computation and Language
|
Graph Transformer Networks with Syntactic and Semantic Structures for
Event Argument Extraction
|
The goal of Event Argument Extraction (EAE) is to find the role of each
entity mention for a given event trigger word. It has been shown in the
previous works that the syntactic structures of the sentences are helpful for
the deep learning models for EAE. However, a major problem in such prior works
is that they fail to exploit the semantic structures of the sentences to induce
effective representations for EAE. Consequently, in this work, we propose a
novel model for EAE that exploits both syntactic and semantic structures of the
sentences with the Graph Transformer Networks (GTNs) to learn more effective
sentence structures for EAE. In addition, we introduce a novel inductive bias
based on information bottleneck to improve generalization of the EAE models.
Extensive experiments are performed to demonstrate the benefits of the proposed
model, leading to state-of-the-art performance for EAE on standard datasets.
| 2,020 |
Computation and Language
|
Robust and Consistent Estimation of Word Embedding for Bangla Language
by fine-tuning Word2Vec Model
|
Word embedding or vector representation of word holds syntactical and
semantic characteristics of a word which can be an informative feature for any
machine learning-based models of natural language processing. There are several
deep learning-based models for the vectorization of words like word2vec,
fasttext, gensim, glove, etc. In this study, we analyze word2vec model for
learning word vectors by tuning different hyper-parameters and present the most
effective word embedding for Bangla language. For testing the performances of
different word embeddings generated by fine-tuning of word2vec model, we
perform both intrinsic and extrinsic evaluations. We cluster the word vectors
to examine the relational similarity of words for intrinsic evaluation and also
use different word embeddings as the feature of news article classifier for
extrinsic evaluation. From our experiment, we discover that the word vectors
with 300 dimensions, generated from "skip-gram" method of word2vec model using
the sliding window size of 4, are giving the most robust vector representations
for Bangla language.
| 2,021 |
Computation and Language
|
TPLinker: Single-stage Joint Extraction of Entities and Relations
Through Token Pair Linking
|
Extracting entities and relations from unstructured text has attracted
increasing attention in recent years but remains challenging, due to the
intrinsic difficulty in identifying overlapping relations with shared entities.
Prior works show that joint learning can result in a noticeable performance
gain. However, they usually involve sequential interrelated steps and suffer
from the problem of exposure bias. At training time, they predict with the
ground truth conditions while at inference it has to make extraction from
scratch. This discrepancy leads to error accumulation. To mitigate the issue,
we propose in this paper a one-stage joint extraction model, namely, TPLinker,
which is capable of discovering overlapping relations sharing one or both
entities while immune from the exposure bias. TPLinker formulates joint
extraction as a token pair linking problem and introduces a novel handshaking
tagging scheme that aligns the boundary tokens of entity pairs under each
relation type. Experiment results show that TPLinker performs significantly
better on overlapping and multiple relation extraction, and achieves
state-of-the-art performance on two public datasets.
| 2,020 |
Computation and Language
|
Syllabification of the Divine Comedy
|
We provide a syllabification algorithm for the Divine Comedy using techniques
from probabilistic and constraint programming. We particularly focus on the
synalephe, addressed in terms of the "propensity" of a word to take part in a
synalephe with adjacent words. We jointly provide an online vocabulary
containing, for each word, information about its syllabification, the location
of the tonic accent, and the aforementioned synalephe propensity, on the left
and right sides. The algorithm is intrinsically nondeterministic, producing
different possible syllabifications for each verse, with different likelihoods;
metric constraints relative to accents on the 10th, 4th and 6th syllables are
used to further reduce the solution space. The most likely syllabification is
hence returned as output. We believe that this work could be a major milestone
for a lot of different investigations. From the point of view of digital
humanities it opens new perspectives on computer assisted analysis of digital
sources, comprising automated detection of anomalous and problematic cases,
metric clustering of verses and their categorization, or more foundational
investigations addressing e.g. the phonetic roles of consonants and vowels.
From the point of view of text processing and deep learning, information about
syllabification and the location of accents opens a wide range of exciting
perspectives, from the possibility of automatic learning syllabification of
words and verses, to the improvement of generative models, aware of metric
issues, and more respectful of the expected musicality.
| 2,020 |
Computation and Language
|
Meta-Learning for Neural Relation Classification with Distant
Supervision
|
Distant supervision provides a means to create a large number of weakly
labeled data at low cost for relation classification. However, the resulting
labeled instances are very noisy, containing data with wrong labels. Many
approaches have been proposed to select a subset of reliable instances for
neural model training, but they still suffer from noisy labeling problem or
underutilization of the weakly-labeled data. To better select more reliable
training instances, we introduce a small amount of manually labeled data as
reference to guide the selection process. In this paper, we propose a
meta-learning based approach, which learns to reweight noisy training data
under the guidance of reference data. As the clean reference data is usually
very small, we propose to augment it by dynamically distilling the most
reliable elite instances from the noisy data. Experiments on several datasets
demonstrate that the reference data can effectively guide the selection of
training data, and our augmented approach consistently improves the performance
of relation classification comparing to the existing state-of-the-art methods.
| 2,020 |
Computation and Language
|
Hierarchical Metadata-Aware Document Categorization under Weak
Supervision
|
Categorizing documents into a given label hierarchy is intuitively appealing
due to the ubiquity of hierarchical topic structures in massive text corpora.
Although related studies have achieved satisfying performance in fully
supervised hierarchical document classification, they usually require massive
human-annotated training data and only utilize text information. However, in
many domains, (1) annotations are quite expensive where very few training
samples can be acquired; (2) documents are accompanied by metadata information.
Hence, this paper studies how to integrate the label hierarchy, metadata, and
text signals for document categorization under weak supervision. We develop
HiMeCat, an embedding-based generative framework for our task. Specifically, we
propose a novel joint representation learning module that allows simultaneous
modeling of category dependencies, metadata information and textual semantics,
and we introduce a data augmentation module that hierarchically synthesizes
training documents to complement the original, small-scale training set. Our
experiments demonstrate a consistent improvement of HiMeCat over competitive
baselines and validate the contribution of our representation learning and data
augmentation modules.
| 2,023 |
Computation and Language
|
Interpreting convolutional networks trained on textual data
|
There have been many advances in the artificial intelligence field due to the
emergence of deep learning. In almost all sub-fields, artificial neural
networks have reached or exceeded human-level performance. However, most of the
models are not interpretable. As a result, it is hard to trust their decisions,
especially in life and death scenarios. In recent years, there has been a
movement toward creating explainable artificial intelligence, but most work to
date has concentrated on image processing models, as it is easier for humans to
perceive visual patterns. There has been little work in other fields like
natural language processing. In this paper, we train a convolutional model on
textual data and analyze the global logic of the model by studying its filter
values. In the end, we find the most important words in our corpus to our
models logic and remove the rest (95%). New models trained on just the 5% most
important words can achieve the same performance as the original model while
reducing training time by more than half. Approaches such as this will help us
to understand NLP models, explain their decisions according to their word
choices, and improve them by finding blind spots and biases.
| 2,021 |
Computation and Language
|
Curious Case of Language Generation Evaluation Metrics: A Cautionary
Tale
|
Automatic evaluation of language generation systems is a well-studied problem
in Natural Language Processing. While novel metrics are proposed every year, a
few popular metrics remain as the de facto metrics to evaluate tasks such as
image captioning and machine translation, despite their known limitations. This
is partly due to ease of use, and partly because researchers expect to see them
and know how to interpret them. In this paper, we urge the community for more
careful consideration of how they automatically evaluate their models by
demonstrating important failure cases on multiple datasets, language pairs and
tasks. Our experiments show that metrics (i) usually prefer system outputs to
human-authored texts, (ii) can be insensitive to correct translations of rare
words, (iii) can yield surprisingly high scores when given a single sentence as
system output for the entire test set.
| 2,020 |
Computation and Language
|
UPB at SemEval-2020 Task 12: Multilingual Offensive Language Detection
on Social Media by Fine-tuning a Variety of BERT-based Models
|
Offensive language detection is one of the most challenging problem in the
natural language processing field, being imposed by the rising presence of this
phenomenon in online social media. This paper describes our Transformer-based
solutions for identifying offensive language on Twitter in five languages
(i.e., English, Arabic, Danish, Greek, and Turkish), which was employed in
Subtask A of the Offenseval 2020 shared task. Several neural architectures
(i.e., BERT, mBERT, Roberta, XLM-Roberta, and ALBERT), pre-trained using both
single-language and multilingual corpora, were fine-tuned and compared using
multiple combinations of datasets. Finally, the highest-scoring models were
used for our submissions in the competition, which ranked our team 21st of 85,
28th of 53, 19th of 39, 16th of 37, and 10th of 46 for English, Arabic, Danish,
Greek, and Turkish, respectively.
| 2,020 |
Computation and Language
|
Automatically Identifying Words That Can Serve as Labels for Few-Shot
Text Classification
|
A recent approach for few-shot text classification is to convert textual
inputs to cloze questions that contain some form of task description, process
them with a pretrained language model and map the predicted words to labels.
Manually defining this mapping between words and labels requires both domain
expertise and an understanding of the language model's abilities. To mitigate
this issue, we devise an approach that automatically finds such a mapping given
small amounts of training data. For a number of tasks, the mapping found by our
approach performs almost as well as hand-crafted label-to-word mappings.
| 2,020 |
Computation and Language
|
Dutch Humor Detection by Generating Negative Examples
|
Detecting if a text is humorous is a hard task to do computationally, as it
usually requires linguistic and common sense insights. In machine learning,
humor detection is usually modeled as a binary classification task, trained to
predict if the given text is a joke or another type of text. Rather than using
completely different non-humorous texts, we propose using text generation
algorithms for imitating the original joke dataset to increase the difficulty
for the learning algorithm. We constructed several different joke and non-joke
datasets to test the humor detection abilities of different language
technologies. In particular, we compare the humor detection capabilities of
classic neural network approaches with the state-of-the-art Dutch language
model RobBERT. In doing so, we create and compare the first Dutch humor
detection systems. We found that while other language models perform well when
the non-jokes came from completely different domains, RobBERT was the only one
that was able to distinguish jokes from generated negative examples. This
performance illustrates the usefulness of using text generation to create
negative datasets for humor recognition, and also shows that transformer models
are a large step forward in humor detection.
| 2,020 |
Computation and Language
|
Constraint Translation Candidates: A Bridge between Neural Query
Translation and Cross-lingual Information Retrieval
|
Query translation (QT) is a key component in cross-lingual information
retrieval system (CLIR). With the help of deep learning, neural machine
translation (NMT) has shown promising results on various tasks. However, NMT is
generally trained with large-scale out-of-domain data rather than in-domain
query translation pairs. Besides, the translation model lacks a mechanism at
the inference time to guarantee the generated words to match the search index.
The two shortages of QT result in readable texts for human but inadequate
candidates for the downstream retrieval task. In this paper, we propose a novel
approach to alleviate these problems by limiting the open target vocabulary
search space of QT to a set of important words mined from search index
database. The constraint translation candidates are employed at both of
training and inference time, thus guiding the translation model to learn and
generate well performing target queries. The proposed methods are exploited and
examined in a real-word CLIR system--Aliexpress e-Commerce search engine.
Experimental results demonstrate that our approach yields better performance on
both translation quality and retrieval accuracy than the strong NMT baseline.
| 2,020 |
Computation and Language
|
Exploiting Neural Query Translation into Cross Lingual Information
Retrieval
|
As a crucial role in cross-language information retrieval (CLIR), query
translation has three main challenges: 1) the adequacy of translation; 2) the
lack of in-domain parallel training data; and 3) the requisite of low latency.
To this end, existing CLIR systems mainly exploit statistical-based machine
translation (SMT) rather than the advanced neural machine translation (NMT),
limiting the further improvements on both translation and retrieval quality. In
this paper, we investigate how to exploit neural query translation model into
CLIR system. Specifically, we propose a novel data augmentation method that
extracts query translation pairs according to user clickthrough data, thus to
alleviate the problem of domain-adaptation in NMT. Then, we introduce an
asynchronous strategy which is able to leverage the advantages of the real-time
in SMT and the veracity in NMT. Experimental results reveal that the proposed
approach yields better retrieval quality than strong baselines and can be well
applied into a real-world CLIR system, i.e. Aliexpress e-Commerce search
engine. Readers can examine and test their cases on our website:
https://aliexpress.com .
| 2,020 |
Computation and Language
|
A Corpus for Argumentative Writing Support in German
|
In this paper, we present a novel annotation approach to capture claims and
premises of arguments and their relations in student-written persuasive peer
reviews on business models in German language. We propose an annotation scheme
based on annotation guidelines that allows to model claims and premises as well
as support and attack relations for capturing the structure of argumentative
discourse in student-written peer reviews. We conduct an annotation study with
three annotators on 50 persuasive essays to evaluate our annotation scheme. The
obtained inter-rater agreement of $\alpha=0.57$ for argument components and
$\alpha=0.49$ for argumentative relations indicates that the proposed
annotation scheme successfully guides annotators to moderate agreement.
Finally, we present our freely available corpus of 1,000 persuasive
student-written peer reviews on business models and our annotation guidelines
to encourage future research on the design and development of argumentative
writing support systems for students.
| 2,020 |
Computation and Language
|
A Survey of Embedding Space Alignment Methods for Language and Knowledge
Graphs
|
Neural embedding approaches have become a staple in the fields of computer
vision, natural language processing, and more recently, graph analytics. Given
the pervasive nature of these algorithms, the natural question becomes how to
exploit the embedding spaces to map, or align, embeddings of different data
sources. To this end, we survey the current research landscape on word,
sentence and knowledge graph embedding algorithms. We provide a classification
of the relevant alignment techniques and discuss benchmark datasets used in
this field of research. By gathering these diverse approaches into a singular
survey, we hope to further motivate research into alignment of embedding spaces
of varied data types and sources.
| 2,020 |
Computation and Language
|
Is it Great or Terrible? Preserving Sentiment in Neural Machine
Translation of Arabic Reviews
|
Since the advent of Neural Machine Translation (NMT) approaches there has
been a tremendous improvement in the quality of automatic translation. However,
NMT output still lacks accuracy in some low-resource languages and sometimes
makes major errors that need extensive post-editing. This is particularly
noticeable with texts that do not follow common lexico-grammatical standards,
such as user generated content (UGC). In this paper we investigate the
challenges involved in translating book reviews from Arabic into English, with
particular focus on the errors that lead to incorrect translation of sentiment
polarity. Our study points to the special characteristics of Arabic UGC,
examines the sentiment transfer errors made by Google Translate of Arabic UGC
to English, analyzes why the problem occurs, and proposes an error typology
specific of the translation of Arabic UGC. Our analysis shows that the output
of online translation tools of Arabic UGC can either fail to transfer the
sentiment at all by producing a neutral target text, or completely flips the
sentiment polarity of the target word or phrase and hence delivers a wrong
affect message. We address this problem by fine-tuning an NMT model with
respect to sentiment polarity showing that this approach can significantly help
with correcting sentiment errors detected in the online translation of Arabic
UGC.
| 2,020 |
Computation and Language
|
PowerTransformer: Unsupervised Controllable Revision for Biased Language
Correction
|
Unconscious biases continue to be prevalent in modern text and media, calling
for algorithms that can assist writers with bias correction. For example, a
female character in a story is often portrayed as passive and powerless ("She
daydreams about being a doctor") while a man is portrayed as more proactive and
powerful ("He pursues his dream of being a doctor").
We formulate *Controllable Debiasing*, a new revision task that aims to
rewrite a given text to correct the implicit and potentially undesirable bias
in character portrayals. We then introduce PowerTransformer as an approach that
debiases text through the lens of connotation frames (Sap et al., 2017), which
encode pragmatic knowledge of implied power dynamics with respect to verb
predicates. One key challenge of our task is the lack of parallel corpora. To
address this challenge, we adopt an unsupervised approach using auxiliary
supervision with related tasks such as paraphrasing and self-supervision based
on a reconstruction loss, building on pretrained language models.
Through comprehensive experiments based on automatic and human evaluations,
we demonstrate that our approach outperforms ablations and existing methods
from related tasks. Furthermore, we demonstrate the use of PowerTransformer as
a step toward mitigating the well-documented gender bias in character portrayal
in movie scripts.
| 2,020 |
Computation and Language
|
Semi-Supervised Spoken Language Understanding via Self-Supervised Speech
and Language Model Pretraining
|
Much recent work on Spoken Language Understanding (SLU) is limited in at
least one of three ways: models were trained on oracle text input and neglected
ASR errors, models were trained to predict only intents without the slot
values, or models were trained on a large amount of in-house data. In this
paper, we propose a clean and general framework to learn semantics directly
from speech with semi-supervision from transcribed or untranscribed speech to
address these issues. Our framework is built upon pretrained end-to-end (E2E)
ASR and self-supervised language models, such as BERT, and fine-tuned on a
limited amount of target SLU data. We study two semi-supervised settings for
the ASR component: supervised pretraining on transcribed speech, and
unsupervised pretraining by replacing the ASR encoder with self-supervised
speech representations, such as wav2vec. In parallel, we identify two essential
criteria for evaluating SLU models: environmental noise-robustness and E2E
semantics evaluation. Experiments on ATIS show that our SLU framework with
speech as input can perform on par with those using oracle text as input in
semantics understanding, even though environmental noise is present and a
limited amount of labeled semantics data is available for training.
| 2,020 |
Computation and Language
|
Data Troubles in Sentence Level Confidence Estimation for Machine
Translation
|
The paper investigates the feasibility of confidence estimation for neural
machine translation models operating at the high end of the performance
spectrum. As a side product of the data annotation process necessary for
building such models we propose sentence level accuracy $SACC$ as a simple,
self-explanatory evaluation metric for quality of translation.
Experiments on two different annotator pools, one comprised of non-expert
(crowd-sourced) and one of expert (professional) translators show that $SACC$
can vary greatly depending on the translation proficiency of the annotators,
despite the fact that both pools are about equally reliable according to
Krippendorff's alpha metric; the relatively low values of inter-annotator
agreement confirm the expectation that sentence-level binary labeling $good$ /
$needs\ work$ for translation out of context is very hard.
For an English-Spanish translation model operating at $SACC = 0.89$ according
to a non-expert annotator pool we can derive a confidence estimate that labels
0.5-0.6 of the $good$ translations in an "in-domain" test set with 0.95
Precision. Switching to an expert annotator pool decreases $SACC$ dramatically:
$0.61$ for English-Spanish, measured on the exact same data as above. This
forces us to lower the CE model operating point to 0.9 Precision while labeling
correctly about 0.20-0.25 of the $good$ translations in the data.
We find surprising the extent to which CE depends on the level of proficiency
of the annotator pool used for labeling the data. This leads to an important
recommendation we wish to make when tackling CE modeling in practice: it is
critical to match the end-user expectation for translation quality in the
desired domain with the demands of annotators assigning binary quality labels
to CE training data.
| 2,020 |
Computation and Language
|
Word Frequency Does Not Predict Grammatical Knowledge in Language Models
|
Neural language models learn, to varying degrees of accuracy, the grammatical
properties of natural languages. In this work, we investigate whether there are
systematic sources of variation in the language models' accuracy. Focusing on
subject-verb agreement and reflexive anaphora, we find that certain nouns are
systematically understood better than others, an effect which is robust across
grammatical tasks and different language models. Surprisingly, we find that
across four orders of magnitude, corpus frequency is unrelated to a noun's
performance on grammatical tasks. Finally, we find that a novel noun's
grammatical properties can be few-shot learned from various types of training
data. The results present a paradox: there should be less variation in
grammatical performance than is actually observed.
| 2,020 |
Computation and Language
|
Improved Neural Language Model Fusion for Streaming Recurrent Neural
Network Transducer
|
Recurrent Neural Network Transducer (RNN-T), like most end-to-end speech
recognition model architectures, has an implicit neural network language model
(NNLM) and cannot easily leverage unpaired text data during training. Previous
work has proposed various fusion methods to incorporate external NNLMs into
end-to-end ASR to address this weakness. In this paper, we propose extensions
to these techniques that allow RNN-T to exploit external NNLMs during both
training and inference time, resulting in 13-18% relative Word Error Rate
improvement on Librispeech compared to strong baselines. Furthermore, our
methods do not incur extra algorithmic latency and allow for flexible
plug-and-play of different NNLMs without re-training. We also share in-depth
analysis to better understand the benefits of the different NNLM fusion
methods. Our work provides a reliable technique for leveraging unpaired text
data to significantly improve RNN-T while keeping the system streamable,
flexible, and lightweight.
| 2,020 |
Computation and Language
|
Probing Task-Oriented Dialogue Representation from Language Models
|
This paper investigates pre-trained language models to find out which model
intrinsically carries the most informative representation for task-oriented
dialogue tasks. We approach the problem from two aspects: supervised classifier
probe and unsupervised mutual information probe. We fine-tune a feed-forward
layer as the classifier probe on top of a fixed pre-trained language model with
annotated labels in a supervised way. Meanwhile, we propose an unsupervised
mutual information probe to evaluate the mutual dependence between a real
clustering and a representation clustering. The goals of this empirical paper
are to 1) investigate probing techniques, especially from the unsupervised
mutual information aspect, 2) provide guidelines of pre-trained language model
selection for the dialogue research community, 3) find insights of pre-training
factors for dialogue application that may be the key to success.
| 2,020 |
Computation and Language
|
Improving Limited Labeled Dialogue State Tracking with Self-Supervision
|
Existing dialogue state tracking (DST) models require plenty of labeled data.
However, collecting high-quality labels is costly, especially when the number
of domains increases. In this paper, we address a practical DST problem that is
rarely discussed, i.e., learning efficiently with limited labeled data. We
present and investigate two self-supervised objectives: preserving latent
consistency and modeling conversational behavior. We encourage a DST model to
have consistent latent distributions given a perturbed input, making it more
robust to an unseen scenario. We also add an auxiliary utterance generation
task, modeling a potential correlation between conversational behavior and
dialogue states. The experimental results show that our proposed
self-supervised signals can improve joint goal accuracy by 8.95\% when only 1\%
labeled data is used on the MultiWOZ dataset. We can achieve an additional
1.76\% improvement if some unlabeled data is jointly trained as semi-supervised
learning. We analyze and visualize how our proposed self-supervised signals
help the DST task and hope to stimulate future data-efficient DST research.
| 2,020 |
Computation and Language
|
Reading Between the Lines: Exploring Infilling in Visual Narratives
|
Generating long form narratives such as stories and procedures from multiple
modalities has been a long standing dream for artificial intelligence. In this
regard, there is often crucial subtext that is derived from the surrounding
contexts. The general seq2seq training methods render the models shorthanded
while attempting to bridge the gap between these neighbouring contexts. In this
paper, we tackle this problem by using \textit{infilling} techniques involving
prediction of missing steps in a narrative while generating textual
descriptions from a sequence of images. We also present a new large scale
\textit{visual procedure telling} (ViPT) dataset with a total of 46,200
procedures and around 340k pairwise images and textual descriptions that is
rich in such contextual dependencies. Generating steps using infilling
technique demonstrates the effectiveness in visual procedures with more
coherent texts. We conclusively show a METEOR score of 27.51 on procedures
which is higher than the state-of-the-art on visual storytelling. We also
demonstrate the effects of interposing new text with missing images during
inference. The code and the dataset will be publicly available at
https://visual-narratives.github.io/Visual-Narratives/.
| 2,020 |
Computation and Language
|
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