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Multilingual Music Genre Embeddings for Effective Cross-Lingual Music
Item Annotation | Annotating music items with music genres is crucial for music recommendation
and information retrieval, yet challenging given that music genres are
subjective concepts. Recently, in order to explicitly consider this
subjectivity, the annotation of music items was modeled as a translation task:
predict for a music item its music genres within a target vocabulary or
taxonomy (tag system) from a set of music genre tags originating from other tag
systems. However, without a parallel corpus, previous solutions could not
handle tag systems in other languages, being limited to the English-language
only. Here, by learning multilingual music genre embeddings, we enable
cross-lingual music genre translation without relying on a parallel corpus.
First, we apply compositionality functions on pre-trained word embeddings to
represent multi-word tags.Second, we adapt the tag representations to the music
domain by leveraging multilingual music genres graphs with a modified
retrofitting algorithm. Experiments show that our method: 1) is effective in
translating music genres across tag systems in multiple languages (English,
French and Spanish); 2) outperforms the previous baseline in an
English-language multi-source translation task. We publicly release the new
multilingual data and code.
| 2,020 | Computation and Language |
GLUCOSE: GeneraLized and COntextualized Story Explanations | When humans read or listen, they make implicit commonsense inferences that
frame their understanding of what happened and why. As a step toward AI systems
that can build similar mental models, we introduce GLUCOSE, a large-scale
dataset of implicit commonsense causal knowledge, encoded as causal
mini-theories about the world, each grounded in a narrative context. To
construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions
of causal explanation, focusing on events, states, motivations, and emotions.
Each GLUCOSE entry includes a story-specific causal statement paired with an
inference rule generalized from the statement. This paper details two concrete
contributions. First, we present our platform for effectively crowdsourcing
GLUCOSE data at scale, which uses semi-structured templates to elicit causal
explanations. Using this platform, we collected a total of ~670K specific
statements and general rules that capture implicit commonsense knowledge about
everyday situations. Second, we show that existing knowledge resources and
pretrained language models do not include or readily predict GLUCOSE's rich
inferential content. However, when state-of-the-art neural models are trained
on this knowledge, they can start to make commonsense inferences on unseen
stories that match humans' mental models.
| 2,020 | Computation and Language |
Generative Language-Grounded Policy in Vision-and-Language Navigation
with Bayes' Rule | Vision-and-language navigation (VLN) is a task in which an agent is embodied
in a realistic 3D environment and follows an instruction to reach the goal
node. While most of the previous studies have built and investigated a
discriminative approach, we notice that there are in fact two possible
approaches to building such a VLN agent: discriminative \textit{and}
generative. In this paper, we design and investigate a generative
language-grounded policy which uses a language model to compute the
distribution over all possible instructions i.e. all possible sequences of
vocabulary tokens given action and the transition history. In experiments, we
show that the proposed generative approach outperforms the discriminative
approach in the Room-2-Room (R2R) and Room-4-Room (R4R) datasets, especially in
the unseen environments. We further show that the combination of the generative
and discriminative policies achieves close to the state-of-the art results in
the R2R dataset, demonstrating that the generative and discriminative policies
capture the different aspects of VLN.
| 2,020 | Computation and Language |
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark | We present CoDEx, a set of knowledge graph completion datasets extracted from
Wikidata and Wikipedia that improve upon existing knowledge graph completion
benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises
three knowledge graphs varying in size and structure, multilingual descriptions
of entities and relations, and tens of thousands of hard negative triples that
are plausible but verified to be false. To characterize CoDEx, we contribute
thorough empirical analyses and benchmarking experiments. First, we analyze
each CoDEx dataset in terms of logical relation patterns. Next, we report
baseline link prediction and triple classification results on CoDEx for five
extensively tuned embedding models. Finally, we differentiate CoDEx from the
popular FB15K-237 knowledge graph completion dataset by showing that CoDEx
covers more diverse and interpretable content, and is a more difficult link
prediction benchmark. Data, code, and pretrained models are available at
https://bit.ly/2EPbrJs.
| 2,020 | Computation and Language |
Text Generation by Learning from Demonstrations | Current approaches to text generation largely rely on autoregressive models
and maximum likelihood estimation. This paradigm leads to (i) diverse but
low-quality samples due to mismatched learning objective and evaluation metric
(likelihood vs. quality) and (ii) exposure bias due to mismatched history
distributions (gold vs. model-generated). To alleviate these problems, we frame
text generation as an offline reinforcement learning (RL) problem with expert
demonstrations (i.e., the reference), where the goal is to maximize quality
given model-generated histories. We propose GOLD (generation by off-policy
learning from demonstrations): an easy-to-optimize algorithm that learns from
the demonstrations by importance weighting. Intuitively, GOLD upweights
confident tokens and downweights unconfident ones in the reference during
training, avoiding optimization issues faced by prior RL approaches that rely
on online data collection. According to both automatic and human evaluation,
models trained by GOLD outperform those trained by MLE and policy gradient on
summarization, question generation, and machine translation. Further, our
models are less sensitive to decoding algorithms and alleviate exposure bias.
| 2,021 | Computation and Language |
How to marry a star: probabilistic constraints for meaning in context | In this paper, we derive a notion of 'word meaning in context' that
characterizes meaning as both intensional and conceptual. We introduce a
framework for specifying local as well as global constraints on word meaning in
context, together with their interactions, thus modelling the wide range of
lexical shifts and ambiguities observed in utterance interpretation. We
represent sentence meaning as a 'situation description system', a probabilistic
model which takes utterance understanding to be the mental process of
describing to oneself one or more situations that would account for an observed
utterance. We show how the system can be implemented in practice, and apply it
to examples containing various contextualisation phenomena.
| 2,022 | Computation and Language |
Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based
Sentiment Analysis | Aspect-based sentiment analysis (ABSA) aims to predict the sentiment towards
a specific aspect in the text. However, existing ABSA test sets cannot be used
to probe whether a model can distinguish the sentiment of the target aspect
from the non-target aspects. To solve this problem, we develop a simple but
effective approach to enrich ABSA test sets. Specifically, we generate new
examples to disentangle the confounding sentiments of the non-target aspects
from the target aspect's sentiment. Based on the SemEval 2014 dataset, we
construct the Aspect Robustness Test Set (ARTS) as a comprehensive probe of the
aspect robustness of ABSA models. Over 92% data of ARTS show high fluency and
desired sentiment on all aspects by human evaluation. Using ARTS, we analyze
the robustness of nine ABSA models, and observe, surprisingly, that their
accuracy drops by up to 69.73%. We explore several ways to improve aspect
robustness, and find that adversarial training can improve models' performance
on ARTS by up to 32.85%. Our code and new test set are available at
https://github.com/zhijing-jin/ARTS_TestSet
| 2,020 | Computation and Language |
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise
ThingTalk Representation | Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ)
conversations suffer from the difficulty in acquiring a high-quality, manually
annotated training set. Approaches based only on dialogue synthesis are
insufficient, as dialogues generated from state-machine based models are poor
approximations of real-life conversations. Furthermore, previously proposed
dialogue state representations are ambiguous and lack the precision necessary
for building an effective agent. This paper proposes a new dialogue
representation and a sample-efficient methodology that can predict precise
dialogue states in WOZ conversations. We extended the ThingTalk representation
to capture all information an agent needs to respond properly. Our training
strategy is sample-efficient: we combine (1) fewshot data sparsely sampling the
full dialogue space and (2) synthesized data covering a subset space of
dialogues generated by a succinct state-based dialogue model. The completeness
of the extended ThingTalk language is demonstrated with a fully operational
agent, which is also used in training data synthesis. We demonstrate the
effectiveness of our methodology on MultiWOZ 3.0, a reannotation of the
MultiWOZ 2.1 dataset in ThingTalk. ThingTalk can represent 98% of the test
turns, while the simulator can emulate 85% of the validation set. We train a
contextual semantic parser using our strategy, and obtain 79% turn-by-turn
exact match accuracy on the reannotated test set.
| 2,022 | Computation and Language |
Multi-modal Summarization for Video-containing Documents | Summarization of multimedia data becomes increasingly significant as it is
the basis for many real-world applications, such as question answering, Web
search, and so forth. Most existing multi-modal summarization works however
have used visual complementary features extracted from images rather than
videos, thereby losing abundant information. Hence, we propose a novel
multi-modal summarization task to summarize from a document and its associated
video. In this work, we also build a baseline general model with effective
strategies, i.e., bi-hop attention and improved late fusion mechanisms to
bridge the gap between different modalities, and a bi-stream summarization
strategy to employ text and video summarization simultaneously. Comprehensive
experiments show that the proposed model is beneficial for multi-modal
summarization and superior to existing methods. Moreover, we collect a novel
dataset and it provides a new resource for future study that results from
documents and videos.
| 2,020 | Computation and Language |
Towards Fully 8-bit Integer Inference for the Transformer Model | 8-bit integer inference, as a promising direction in reducing both the
latency and storage of deep neural networks, has made great progress recently.
On the other hand, previous systems still rely on 32-bit floating point for
certain functions in complex models (e.g., Softmax in Transformer), and make
heavy use of quantization and de-quantization. In this work, we show that after
a principled modification on the Transformer architecture, dubbed Integer
Transformer, an (almost) fully 8-bit integer inference algorithm Scale
Propagation could be derived. De-quantization is adopted when necessary, which
makes the network more efficient. Our experiments on WMT16 En<->Ro, WMT14
En<->De and En->Fr translation tasks as well as the WikiText-103 language
modelling task show that the fully 8-bit Transformer system achieves comparable
performance with the floating point baseline but requires nearly 4x less memory
footprint.
| 2,020 | Computation and Language |
Self-supervised pre-training and contrastive representation learning for
multiple-choice video QA | Video Question Answering (Video QA) requires fine-grained understanding of
both video and language modalities to answer the given questions. In this
paper, we propose novel training schemes for multiple-choice video question
answering with a self-supervised pre-training stage and a supervised
contrastive learning in the main stage as an auxiliary learning. In the
self-supervised pre-training stage, we transform the original problem format of
predicting the correct answer into the one that predicts the relevant question
to provide a model with broader contextual inputs without any further dataset
or annotation. For contrastive learning in the main stage, we add a masking
noise to the input corresponding to the ground-truth answer, and consider the
original input of the ground-truth answer as a positive sample, while treating
the rest as negative samples. By mapping the positive sample closer to the
masked input, we show that the model performance is improved. We further employ
locally aligned attention to focus more effectively on the video frames that
are particularly relevant to the given corresponding subtitle sentences. We
evaluate our proposed model on highly competitive benchmark datasets related to
multiple-choice video QA: TVQA, TVQA+, and DramaQA. Experimental results show
that our model achieves state-of-the-art performance on all datasets. We also
validate our approaches through further analyses.
| 2,020 | Computation and Language |
Efficient Transformer-based Large Scale Language Representations using
Hardware-friendly Block Structured Pruning | Pre-trained large-scale language models have increasingly demonstrated high
accuracy on many natural language processing (NLP) tasks. However, the limited
weight storage and computational speed on hardware platforms have impeded the
popularity of pre-trained models, especially in the era of edge computing. In
this work, we propose an efficient transformer-based large-scale language
representation using hardware-friendly block structure pruning. We incorporate
the reweighted group Lasso into block-structured pruning for optimization.
Besides the significantly reduced weight storage and computation, the proposed
approach achieves high compression rates. Experimental results on different
models (BERT, RoBERTa, and DistilBERT) on the General Language Understanding
Evaluation (GLUE) benchmark tasks show that we achieve up to 5.0x with zero or
minor accuracy degradation on certain task(s). Our proposed method is also
orthogonal to existing compact pre-trained language models such as DistilBERT
using knowledge distillation, since a further 1.79x average compression rate
can be achieved on top of DistilBERT with zero or minor accuracy degradation.
It is suitable to deploy the final compressed model on resource-constrained
edge devices.
| 2,020 | Computation and Language |
On the Transferability of Minimal Prediction Preserving Inputs in
Question Answering | Recent work (Feng et al., 2018) establishes the presence of short,
uninterpretable input fragments that yield high confidence and accuracy in
neural models. We refer to these as Minimal Prediction Preserving Inputs
(MPPIs). In the context of question answering, we investigate competing
hypotheses for the existence of MPPIs, including poor posterior calibration of
neural models, lack of pretraining, and "dataset bias" (where a model learns to
attend to spurious, non-generalizable cues in the training data). We discover a
perplexing invariance of MPPIs to random training seed, model architecture,
pretraining, and training domain. MPPIs demonstrate remarkable transferability
across domains achieving significantly higher performance than comparably short
queries. Additionally, penalizing over-confidence on MPPIs fails to improve
either generalization or adversarial robustness. These results suggest the
interpretability of MPPIs is insufficient to characterize generalization
capacity of these models. We hope this focused investigation encourages more
systematic analysis of model behavior outside of the human interpretable
distribution of examples.
| 2,021 | Computation and Language |
Code-switching pre-training for neural machine translation | This paper proposes a new pre-training method, called Code-Switching
Pre-training (CSP for short) for Neural Machine Translation (NMT). Unlike
traditional pre-training method which randomly masks some fragments of the
input sentence, the proposed CSP randomly replaces some words in the source
sentence with their translation words in the target language. Specifically, we
firstly perform lexicon induction with unsupervised word embedding mapping
between the source and target languages, and then randomly replace some words
in the input sentence with their translation words according to the extracted
translation lexicons. CSP adopts the encoder-decoder framework: its encoder
takes the code-mixed sentence as input, and its decoder predicts the replaced
fragment of the input sentence. In this way, CSP is able to pre-train the NMT
model by explicitly making the most of the cross-lingual alignment information
extracted from the source and target monolingual corpus. Additionally, we
relieve the pretrain-finetune discrepancy caused by the artificial symbols like
[mask]. To verify the effectiveness of the proposed method, we conduct
extensive experiments on unsupervised and supervised NMT. Experimental results
show that CSP achieves significant improvements over baselines without
pre-training or with other pre-training methods.
| 2,020 | Computation and Language |
A Deep Learning Approach to Geographical Candidate Selection through
Toponym Matching | Recognizing toponyms and resolving them to their real-world referents is
required for providing advanced semantic access to textual data. This process
is often hindered by the high degree of variation in toponyms. Candidate
selection is the task of identifying the potential entities that can be
referred to by a toponym previously recognized. While it has traditionally
received little attention in the research community, it has been shown that
candidate selection has a significant impact on downstream tasks (i.e. entity
resolution), especially in noisy or non-standard text. In this paper, we
introduce a flexible deep learning method for candidate selection through
toponym matching, using state-of-the-art neural network architectures. We
perform an intrinsic toponym matching evaluation based on several new realistic
datasets, which cover various challenging scenarios (cross-lingual and regional
variations, as well as OCR errors). We report its performance on candidate
selection in the context of the downstream task of toponym resolution, both on
existing datasets and on a new manually-annotated resource of
nineteenth-century English OCR'd text.
| 2,020 | Computation and Language |
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief
States towards Semi-Supervised Learning | Structured belief states are crucial for user goal tracking and database
query in task-oriented dialog systems. However, training belief trackers often
requires expensive turn-level annotations of every user utterance. In this
paper we aim at alleviating the reliance on belief state labels in building
end-to-end dialog systems, by leveraging unlabeled dialog data towards
semi-supervised learning. We propose a probabilistic dialog model, called the
LAtent BElief State (LABES) model, where belief states are represented as
discrete latent variables and jointly modeled with system responses given user
inputs. Such latent variable modeling enables us to develop semi-supervised
learning under the principled variational learning framework. Furthermore, we
introduce LABES-S2S, which is a copy-augmented Seq2Seq model instantiation of
LABES. In supervised experiments, LABES-S2S obtains strong results on three
benchmark datasets of different scales. In utilizing unlabeled dialog data,
semi-supervised LABES-S2S significantly outperforms both supervised-only and
semi-supervised baselines. Remarkably, we can reduce the annotation demands to
50% without performance loss on MultiWOZ.
| 2,020 | Computation and Language |
Multi$^2$OIE: Multilingual Open Information Extraction Based on
Multi-Head Attention with BERT | In this paper, we propose Multi$^2$OIE, which performs open information
extraction (open IE) by combining BERT with multi-head attention. Our model is
a sequence-labeling system with an efficient and effective argument extraction
method. We use a query, key, and value setting inspired by the Multimodal
Transformer to replace the previously used bidirectional long short-term memory
architecture with multi-head attention. Multi$^2$OIE outperforms existing
sequence-labeling systems with high computational efficiency on two benchmark
evaluation datasets, Re-OIE2016 and CaRB. Additionally, we apply the proposed
method to multilingual open IE using multilingual BERT. Experimental results on
new benchmark datasets introduced for two languages (Spanish and Portuguese)
demonstrate that our model outperforms other multilingual systems without
training data for the target languages.
| 2,020 | Computation and Language |
FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding | Few-shot learning (FSL) is one of the key future steps in machine learning
and has raised a lot of attention. However, in contrast to the rapid
development in other domains, such as Computer Vision, the progress of FSL in
Nature Language Processing (NLP) is much slower. One of the key reasons for
this is the lacking of public benchmarks. NLP FSL researches always report new
results on their own constructed few-shot datasets, which is pretty inefficient
in results comparison and thus impedes cumulative progress. In this paper, we
present FewJoint, a novel Few-Shot Learning benchmark for NLP. Different from
most NLP FSL research that only focus on simple N-classification problems, our
benchmark introduces few-shot joint dialogue language understanding, which
additionally covers the structure prediction and multi-task reliance problems.
This allows our benchmark to reflect the real-word NLP complexity beyond simple
N-classification. Our benchmark is used in the few-shot learning contest of
SMP2020-ECDT task-1. We also provide a compatible FSL platform to ease
experiment set-up.
| 2,020 | Computation and Language |
End-to-End Neural Event Coreference Resolution | Traditional event coreference systems usually rely on pipeline framework and
hand-crafted features, which often face error propagation problem and have poor
generalization ability. In this paper, we propose an End-to-End Event
Coreference approach -- E3C neural network, which can jointly model event
detection and event coreference resolution tasks, and learn to extract features
from raw text automatically. Furthermore, because event mentions are highly
diversified and event coreference is intricately governed by long-distance,
semantic-dependent decisions, a type-guided event coreference mechanism is
further proposed in our E3C neural network. Experiments show that our method
achieves new state-of-the-art performance on two standard datasets.
| 2,020 | Computation and Language |
ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for
Counterfactual Statement Modeling | ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting
counterfactual statements and detecting antecedent and consequence. This paper
describes our system which is based on pre-trained transformers. For the first
subtask, we train several transformer-based classifiers for detecting
counterfactual statements. For the second subtask, we formulate antecedent and
consequence extraction as a query-based question answering problem. The two
subsystems both achieved third place in the evaluation. Our system is openly
released at https://github.com/casnlu/ISCAS-SemEval2020Task5.
| 2,020 | Computation and Language |
DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for
Definition Extraction | We explore the performance of Bidirectional Encoder Representations from
Transformers (BERT) at definition extraction. We further propose a joint model
of BERT and Text Level Graph Convolutional Network so as to incorporate
dependencies into the model. Our proposed model produces better results than
BERT and achieves comparable results to BERT with fine tuned language model in
DeftEval (Task 6 of SemEval 2020), a shared task of classifying whether a
sentence contains a definition or not (Subtask 1).
| 2,020 | Computation and Language |
Generating Label Cohesive and Well-Formed Adversarial Claims | Adversarial attacks reveal important vulnerabilities and flaws of trained
models. One potent type of attack are universal adversarial triggers, which are
individual n-grams that, when appended to instances of a class under attack,
can trick a model into predicting a target class. However, for inference tasks
such as fact checking, these triggers often inadvertently invert the meaning of
instances they are inserted in. In addition, such attacks produce semantically
nonsensical inputs, as they simply concatenate triggers to existing samples.
Here, we investigate how to generate adversarial attacks against fact checking
systems that preserve the ground truth meaning and are semantically valid. We
extend the HotFlip attack algorithm used for universal trigger generation by
jointly minimising the target class loss of a fact checking model and the
entailment class loss of an auxiliary natural language inference model. We then
train a conditional language model to generate semantically valid statements,
which include the found universal triggers. We find that the generated attacks
maintain the directionality and semantic validity of the claim better than
previous work.
| 2,020 | Computation and Language |
AIN: Fast and Accurate Sequence Labeling with Approximate Inference
Network | The linear-chain Conditional Random Field (CRF) model is one of the most
widely-used neural sequence labeling approaches. Exact probabilistic inference
algorithms such as the forward-backward and Viterbi algorithms are typically
applied in training and prediction stages of the CRF model. However, these
algorithms require sequential computation that makes parallelization
impossible. In this paper, we propose to employ a parallelizable approximate
variational inference algorithm for the CRF model. Based on this algorithm, we
design an approximate inference network that can be connected with the encoder
of the neural CRF model to form an end-to-end network, which is amenable to
parallelization for faster training and prediction. The empirical results show
that our proposed approaches achieve a 12.7-fold improvement in decoding speed
with long sentences and a competitive accuracy compared with the traditional
CRF approach.
| 2,020 | Computation and Language |
What if we had no Wikipedia? Domain-independent Term Extraction from a
Large News Corpus | One of the most impressive human endeavors of the past two decades is the
collection and categorization of human knowledge in the free and accessible
format that is Wikipedia. In this work we ask what makes a term worthy of
entering this edifice of knowledge, and having a page of its own in Wikipedia?
To what extent is this a natural product of on-going human discourse and
discussion rather than an idiosyncratic choice of Wikipedia editors?
Specifically, we aim to identify such "wiki-worthy" terms in a massive news
corpus, and see if this can be done with no, or minimal, dependency on actual
Wikipedia entries. We suggest a five-step pipeline for doing so, providing
baseline results for all five, and the relevant datasets for benchmarking them.
Our work sheds new light on the domain-specific Automatic Term Extraction
problem, with the problem at hand being a domain-independent variant of it.
| 2,020 | Computation and Language |
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A
Case Study on CoQA | Many NLP tasks have benefited from transferring knowledge from contextualized
word embeddings, however the picture of what type of knowledge is transferred
is incomplete. This paper studies the types of linguistic phenomena accounted
for by language models in the context of a Conversational Question Answering
(CoQA) task. We identify the problematic areas for the finetuned RoBERTa, BERT
and DistilBERT models through systematic error analysis - basic arithmetic
(counting phrases), compositional semantics (negation and Semantic Role
Labeling), and lexical semantics (surprisal and antonymy). When enhanced with
the relevant linguistic knowledge through multitask learning, the models
improve in performance. Ensembles of the enhanced models yield a boost between
2.2 and 2.7 points in F1 score overall, and up to 42.1 points in F1 on the
hardest question classes. The results show differences in ability to represent
compositional and lexical information between RoBERTa, BERT and DistilBERT.
| 2,020 | Computation and Language |
More Embeddings, Better Sequence Labelers? | Recent work proposes a family of contextual embeddings that significantly
improves the accuracy of sequence labelers over non-contextual embeddings.
However, there is no definite conclusion on whether we can build better
sequence labelers by combining different kinds of embeddings in various
settings. In this paper, we conduct extensive experiments on 3 tasks over 18
datasets and 8 languages to study the accuracy of sequence labeling with
various embedding concatenations and make three observations: (1) concatenating
more embedding variants leads to better accuracy in rich-resource and
cross-domain settings and some conditions of low-resource settings; (2)
concatenating additional contextual sub-word embeddings with contextual
character embeddings hurts the accuracy in extremely low-resource settings; (3)
based on the conclusion of (1), concatenating additional similar contextual
embeddings cannot lead to further improvements. We hope these conclusions can
help people build stronger sequence labelers in various settings.
| 2,021 | Computation and Language |
Evaluating Interactive Summarization: an Expansion-Based Framework | Allowing users to interact with multi-document summarizers is a promising
direction towards improving and customizing summary results. Different ideas
for interactive summarization have been proposed in previous work but these
solutions are highly divergent and incomparable. In this paper, we develop an
end-to-end evaluation framework for expansion-based interactive summarization,
which considers the accumulating information along an interactive session. Our
framework includes a procedure of collecting real user sessions and evaluation
measures relying on standards, but adapted to reflect interaction. All of our
solutions are intended to be released publicly as a benchmark, allowing
comparison of future developments in interactive summarization. We demonstrate
the use of our framework by evaluating and comparing baseline implementations
that we developed for this purpose, which will serve as part of our benchmark.
Our extensive experimentation and analysis of these systems motivate our design
choices and support the viability of our framework.
| 2,020 | Computation and Language |
Modeling Task Effects on Meaning Representation in the Brain via
Zero-Shot MEG Prediction | How meaning is represented in the brain is still one of the big open
questions in neuroscience. Does a word (e.g., bird) always have the same
representation, or does the task under which the word is processed alter its
representation (answering "can you eat it?" versus "can it fly?")? The brain
activity of subjects who read the same word while performing different semantic
tasks has been shown to differ across tasks. However, it is still not
understood how the task itself contributes to this difference. In the current
work, we study Magnetoencephalography (MEG) brain recordings of participants
tasked with answering questions about concrete nouns. We investigate the effect
of the task (i.e. the question being asked) on the processing of the concrete
noun by predicting the millisecond-resolution MEG recordings as a function of
both the semantics of the noun and the task. Using this approach, we test
several hypotheses about the task-stimulus interactions by comparing the
zero-shot predictions made by these hypotheses for novel tasks and nouns not
seen during training. We find that incorporating the task semantics
significantly improves the prediction of MEG recordings, across participants.
The improvement occurs 475-550ms after the participants first see the word,
which corresponds to what is considered to be the ending time of semantic
processing for a word. These results suggest that only the end of semantic
processing of a word is task-dependent, and pose a challenge for future
research to formulate new hypotheses for earlier task effects as a function of
the task and stimuli.
| 2,020 | Computation and Language |
A Computational Approach to Understanding Empathy Expressed in
Text-Based Mental Health Support | Empathy is critical to successful mental health support. Empathy measurement
has predominantly occurred in synchronous, face-to-face settings, and may not
translate to asynchronous, text-based contexts. Because millions of people use
text-based platforms for mental health support, understanding empathy in these
contexts is crucial. In this work, we present a computational approach to
understanding how empathy is expressed in online mental health platforms. We
develop a novel unifying theoretically-grounded framework for characterizing
the communication of empathy in text-based conversations. We collect and share
a corpus of 10k (post, response) pairs annotated using this empathy framework
with supporting evidence for annotations (rationales). We develop a multi-task
RoBERTa-based bi-encoder model for identifying empathy in conversations and
extracting rationales underlying its predictions. Experiments demonstrate that
our approach can effectively identify empathic conversations. We further apply
this model to analyze 235k mental health interactions and show that users do
not self-learn empathy over time, revealing opportunities for empathy training
and feedback.
| 2,020 | Computation and Language |
Self-Supervised Meta-Learning for Few-Shot Natural Language
Classification Tasks | Self-supervised pre-training of transformer models has revolutionized NLP
applications. Such pre-training with language modeling objectives provides a
useful initial point for parameters that generalize well to new tasks with
fine-tuning. However, fine-tuning is still data inefficient -- when there are
few labeled examples, accuracy can be low. Data efficiency can be improved by
optimizing pre-training directly for future fine-tuning with few examples; this
can be treated as a meta-learning problem. However, standard meta-learning
techniques require many training tasks in order to generalize; unfortunately,
finding a diverse set of such supervised tasks is usually difficult. This paper
proposes a self-supervised approach to generate a large, rich, meta-learning
task distribution from unlabeled text. This is achieved using a cloze-style
objective, but creating separate multi-class classification tasks by gathering
tokens-to-be blanked from among only a handful of vocabulary terms. This yields
as many unique meta-training tasks as the number of subsets of vocabulary
terms. We meta-train a transformer model on this distribution of tasks using a
recent meta-learning framework. On 17 NLP tasks, we show that this
meta-training leads to better few-shot generalization than language-model
pre-training followed by finetuning. Furthermore, we show how the
self-supervised tasks can be combined with supervised tasks for meta-learning,
providing substantial accuracy gains over previous supervised meta-learning.
| 2,020 | Computation and Language |
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using
Human Phenotype Ontology | Automatic phenotype concept recognition from unstructured text remains a
challenging task in biomedical text mining research. Previous works that
address the task typically use dictionary-based matching methods, which can
achieve high precision but suffer from lower recall. Recently, machine
learning-based methods have been proposed to identify biomedical concepts,
which can recognize more unseen concept synonyms by automatic feature learning.
However, most methods require large corpora of manually annotated data for
model training, which is difficult to obtain due to the high cost of human
annotation. In this paper, we propose PhenoTagger, a hybrid method that
combines both dictionary and machine learning-based methods to recognize Human
Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use
all concepts and synonyms in HPO to construct a dictionary, which is then used
to automatically build a distantly supervised training dataset for machine
learning. Next, a cutting-edge deep learning model is trained to classify each
candidate phrase (n-gram from input sentence) into a corresponding concept
label. Finally, the dictionary and machine learning-based prediction results
are combined for improved performance. Our method is validated with two HPO
corpora, and the results show that PhenoTagger compares favorably to previous
methods. In addition, to demonstrate the generalizability of our method, we
retrained PhenoTagger using the disease ontology MEDIC for disease concept
recognition to investigate the effect of training on different ontologies.
Experimental results on the NCBI disease corpus show that PhenoTagger without
requiring manually annotated training data achieves competitive performance as
compared with state-of-the-art supervised methods.
| 2,021 | Computation and Language |
Structured Attention for Unsupervised Dialogue Structure Induction | Inducing a meaningful structural representation from one or a set of
dialogues is a crucial but challenging task in computational linguistics.
Advancement made in this area is critical for dialogue system design and
discourse analysis. It can also be extended to solve grammatical inference. In
this work, we propose to incorporate structured attention layers into a
Variational Recurrent Neural Network (VRNN) model with discrete latent states
to learn dialogue structure in an unsupervised fashion. Compared to a vanilla
VRNN, structured attention enables a model to focus on different parts of the
source sentence embeddings while enforcing a structural inductive bias.
Experiments show that on two-party dialogue datasets, VRNN with structured
attention learns semantic structures that are similar to templates used to
generate this dialogue corpus. While on multi-party dialogue datasets, our
model learns an interactive structure demonstrating its capability of
distinguishing speakers or addresses, automatically disentangling dialogues
without explicit human annotation.
| 2,021 | Computation and Language |
Generation-Augmented Retrieval for Open-domain Question Answering | We propose Generation-Augmented Retrieval (GAR) for answering open-domain
questions, which augments a query through text generation of heuristically
discovered relevant contexts without external resources as supervision. We
demonstrate that the generated contexts substantially enrich the semantics of
the queries and GAR with sparse representations (BM25) achieves comparable or
better performance than state-of-the-art dense retrieval methods such as DPR.
We show that generating diverse contexts for a query is beneficial as fusing
their results consistently yields better retrieval accuracy. Moreover, as
sparse and dense representations are often complementary, GAR can be easily
combined with DPR to achieve even better performance. GAR achieves
state-of-the-art performance on Natural Questions and TriviaQA datasets under
the extractive QA setup when equipped with an extractive reader, and
consistently outperforms other retrieval methods when the same generative
reader is used.
| 2,021 | Computation and Language |
Small but Mighty: New Benchmarks for Split and Rephrase | Split and Rephrase is a text simplification task of rewriting a complex
sentence into simpler ones. As a relatively new task, it is paramount to ensure
the soundness of its evaluation benchmark and metric. We find that the widely
used benchmark dataset universally contains easily exploitable syntactic cues
caused by its automatic generation process. Taking advantage of such cues, we
show that even a simple rule-based model can perform on par with the
state-of-the-art model. To remedy such limitations, we collect and release two
crowdsourced benchmark datasets. We not only make sure that they contain
significantly more diverse syntax, but also carefully control for their quality
according to a well-defined set of criteria. While no satisfactory automatic
metric exists, we apply fine-grained manual evaluation based on these criteria
using crowdsourcing, showing that our datasets better represent the task and
are significantly more challenging for the models.
| 2,020 | Computation and Language |
NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is
Not Necessarily Informative | Millions of people around the world are sharing COVID-19 related information
on social media platforms. Since not all the information shared on the social
media is useful, a machine learning system to identify informative posts can
help users in finding relevant information. In this paper, we present a BERT
classifier system for W-NUT2020 Shared Task 2: Identification of Informative
COVID-19 English Tweets. Further, we show that BERT exploits some easy signals
to identify informative tweets, and adding simple patterns to uninformative
tweets drastically degrades BERT performance. In particular, simply adding 10
deaths to tweets in dev set, reduces BERT F1- score from 92.63 to 7.28. We also
propose a simple data augmentation technique that helps in improving the
robustness and generalization ability of the BERT classifier.
| 2,020 | Computation and Language |
Unsupervised Parallel Corpus Mining on Web Data | With a large amount of parallel data, neural machine translation systems are
able to deliver human-level performance for sentence-level translation.
However, it is costly to label a large amount of parallel data by humans. In
contrast, there is a large-scale of parallel corpus created by humans on the
Internet. The major difficulty to utilize them is how to filter them out from
the noise website environments. Current parallel data mining methods all
require labeled parallel data as the training source. In this paper, we present
a pipeline to mine the parallel corpus from the Internet in an unsupervised
manner. On the widely used WMT'14 English-French and WMT'16 English-German
benchmarks, the machine translator trained with the data extracted by our
pipeline achieves very close performance to the supervised results. On the
WMT'16 English-Romanian and Romanian-English benchmarks, our system produces
new state-of-the-art results, 39.81 and 38.95 BLEU scores, even compared with
supervised approaches.
| 2,020 | Computation and Language |
fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP | We present fastHan, an open-source toolkit for four basic tasks in Chinese
natural language processing: Chinese word segmentation (CWS), Part-of-Speech
(POS) tagging, named entity recognition (NER), and dependency parsing. The
backbone of fastHan is a multi-task model based on a pruned BERT, which uses
the first 8 layers in BERT. We also provide a 4-layer base model compressed
from the 8-layer model. The joint-model is trained and evaluated on 13 corpora
of four tasks, yielding near state-of-the-art (SOTA) performance in dependency
parsing and NER, achieving SOTA performance in CWS and POS. Besides, fastHan's
transferability is also strong, performing much better than popular
segmentation tools on a non-training corpus. To better meet the need of
practical application, we allow users to use their own labeled data to further
fine-tune fastHan. In addition to its small size and excellent performance,
fastHan is user-friendly. Implemented as a python package, fastHan isolates
users from the internal technical details and is convenient to use. The project
is released on Github.
| 2,021 | Computation and Language |
Hierarchical GPT with Congruent Transformers for Multi-Sentence Language
Models | We report a GPT-based multi-sentence language model for dialogue generation
and document understanding. First, we propose a hierarchical GPT which consists
of three blocks, i.e., a sentence encoding block, a sentence generating block,
and a sentence decoding block. The sentence encoding and decoding blocks are
basically the encoder-decoder blocks of the standard Transformers, which work
on each sentence independently. The sentence generating block is inserted
between the encoding and decoding blocks, and generates the next sentence
embedding vector from the previous sentence embedding vectors. We believe it is
the way human make conversation and understand paragraphs and documents. Since
each sentence may consist of fewer words, the sentence encoding and decoding
Transformers can use much smaller dimensional embedding vectors. Secondly, we
note the attention in the Transformers utilizes the inner-product similarity
measure. Therefore, to compare the two vectors in the same space, we set the
transform matrices for queries and keys to be the same. Otherwise, the
similarity concept is incongruent. We report experimental results to show that
these two modifications increase the language model performance for tasks with
multiple sentences.
| 2,020 | Computation and Language |
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting
Local Structures | Understanding a medical conversation between a patient and a physician poses
a unique natural language understanding challenge since it combines elements of
standard open ended conversation with very domain specific elements that
require expertise and medical knowledge. Summarization of medical conversations
is a particularly important aspect of medical conversation understanding since
it addresses a very real need in medical practice: capturing the most important
aspects of a medical encounter so that they can be used for medical decision
making and subsequent follow ups.
In this paper we present a novel approach to medical conversation
summarization that leverages the unique and independent local structures
created when gathering a patient's medical history. Our approach is a variation
of the pointer generator network where we introduce a penalty on the generator
distribution, and we explicitly model negations. The model also captures
important properties of medical conversations such as medical knowledge coming
from standardized medical ontologies better than when those concepts are
introduced explicitly. Through evaluation by doctors, we show that our approach
is preferred on twice the number of summaries to the baseline pointer generator
model and captures most or all of the information in 80% of the conversations
making it a realistic alternative to costly manual summarization by medical
experts.
| 2,020 | Computation and Language |
RECON: Relation Extraction using Knowledge Graph Context in a Graph
Neural Network | In this paper, we present a novel method named RECON, that automatically
identifies relations in a sentence (sentential relation extraction) and aligns
to a knowledge graph (KG). RECON uses a graph neural network to learn
representations of both the sentence as well as facts stored in a KG, improving
the overall extraction quality. These facts, including entity attributes
(label, alias, description, instance-of) and factual triples, have not been
collectively used in the state of the art methods. We evaluate the effect of
various forms of representing the KG context on the performance of RECON. The
empirical evaluation on two standard relation extraction datasets shows that
RECON significantly outperforms all state of the art methods on NYT Freebase
and Wikidata datasets. RECON reports 87.23 F1 score (Vs 82.29 baseline) on
Wikidata dataset whereas on NYT Freebase, reported values are 87.5(P@10) and
74.1(P@30) compared to the previous baseline scores of 81.3(P@10) and
63.1(P@30).
| 2,021 | Computation and Language |
The birth of Romanian BERT | Large-scale pretrained language models have become ubiquitous in Natural
Language Processing. However, most of these models are available either in
high-resource languages, in particular English, or as multilingual models that
compromise performance on individual languages for coverage. This paper
introduces Romanian BERT, the first purely Romanian transformer-based language
model, pretrained on a large text corpus. We discuss corpus composition and
cleaning, the model training process, as well as an extensive evaluation of the
model on various Romanian datasets. We open source not only the model itself,
but also a repository that contains information on how to obtain the corpus,
fine-tune and use this model in production (with practical examples), and how
to fully replicate the evaluation process.
| 2,020 | Computation and Language |
Document-level Neural Machine Translation with Document Embeddings | Standard neural machine translation (NMT) is on the assumption of
document-level context independent. Most existing document-level NMT methods
are satisfied with a smattering sense of brief document-level information,
while this work focuses on exploiting detailed document-level context in terms
of multiple forms of document embeddings, which is capable of sufficiently
modeling deeper and richer document-level context. The proposed document-aware
NMT is implemented to enhance the Transformer baseline by introducing both
global and local document-level clues on the source end. Experiments show that
the proposed method significantly improves the translation performance over
strong baselines and other related studies.
| 2,021 | Computation and Language |
FarsTail: A Persian Natural Language Inference Dataset | Natural language inference (NLI) is known as one of the central tasks in
natural language processing (NLP) which encapsulates many fundamental aspects
of language understanding. With the considerable achievements of data-hungry
deep learning methods in NLP tasks, a great amount of effort has been devoted
to develop more diverse datasets for different languages. In this paper, we
present a new dataset for the NLI task in the Persian language, also known as
Farsi, which is one of the dominant languages in the Middle East. This dataset,
named FarsTail, includes 10,367 samples which are provided in both the Persian
language as well as the indexed format to be useful for non-Persian
researchers. The samples are generated from 3,539 multiple-choice questions
with the least amount of annotator interventions in a way similar to the
SciTail dataset. A carefully designed multi-step process is adopted to ensure
the quality of the dataset. We also present the results of traditional and
state-of-the-art methods on FarsTail including different embedding methods such
as word2vec, fastText, ELMo, BERT, and LASER, as well as different modeling
approaches such as DecompAtt, ESIM, HBMP, and ULMFiT to provide a solid
baseline for the future research. The best obtained test accuracy is 83.38%
which shows that there is a big room for improving the current methods to be
useful for real-world NLP applications in different languages. We also
investigate the extent to which the models exploit superficial clues, also
known as dataset biases, in FarsTail, and partition the test set into easy and
hard subsets according to the success of biased models. The dataset is
available at https://github.com/dml-qom/FarsTail
| 2,023 | Computation and Language |
Principal Components of the Meaning | In this paper we argue that (lexical) meaning in science can be represented
in a 13 dimension Meaning Space. This space is constructed using principal
component analysis (singular decomposition) on the matrix of word category
relative information gains, where the categories are those used by the Web of
Science, and the words are taken from a reduced word set from texts in the Web
of Science. We show that this reduced word set plausibly represents all texts
in the corpus, so that the principal component analysis has some objective
meaning with respect to the corpus. We argue that 13 dimensions is adequate to
describe the meaning of scientific texts, and hypothesise about the qualitative
meaning of the principal components.
| 2,020 | Computation and Language |
Generating similes effortlessly like a Pro: A Style Transfer Approach
for Simile Generation | Literary tropes, from poetry to stories, are at the crux of human imagination
and communication. Figurative language such as a simile go beyond plain
expressions to give readers new insights and inspirations. In this paper, we
tackle the problem of simile generation. Generating a simile requires proper
understanding for effective mapping of properties between two concepts. To this
end, we first propose a method to automatically construct a parallel corpus by
transforming a large number of similes collected from Reddit to their literal
counterpart using structured common sense knowledge. We then propose to
fine-tune a pretrained sequence to sequence model, BART~\cite{lewis2019bart},
on the literal-simile pairs to gain generalizability, so that we can generate
novel similes given a literal sentence. Experiments show that our approach
generates $88\%$ novel similes that do not share properties with the training
data. Human evaluation on an independent set of literal statements shows that
our model generates similes better than two literary experts
\textit{37\%}\footnote{We average 32.6\% and 41.3\% for 2 humans.} of the
times, and three baseline systems including a recent metaphor generation model
\textit{71\%}\footnote{We average 82\% ,63\% and 68\% for three baselines.} of
the times when compared pairwise.\footnote{The simile in the title is generated
by our best model. Input: Generating similes effortlessly, output: Generating
similes \textit{like a Pro}.} We also show how replacing literal sentences with
similes from our best model in machine generated stories improves evocativeness
and leads to better acceptance by human judges.
| 2,020 | Computation and Language |
Presenting Simultaneous Translation in Limited Space | Some methods of automatic simultaneous translation of a long-form speech
allow revisions of outputs, trading accuracy for low latency. Deploying these
systems for users faces the problem of presenting subtitles in a limited space,
such as two lines on a television screen. The subtitles must be shown promptly,
incrementally, and with adequate time for reading. We provide an algorithm for
subtitling. Furthermore, we propose a way how to estimate the overall usability
of the combination of automatic translation and subtitling by measuring the
quality, latency, and stability on a test set, and propose an improved measure
for translation latency.
| 2,020 | Computation and Language |
COMET: A Neural Framework for MT Evaluation | We present COMET, a neural framework for training multilingual machine
translation evaluation models which obtains new state-of-the-art levels of
correlation with human judgements. Our framework leverages recent breakthroughs
in cross-lingual pretrained language modeling resulting in highly multilingual
and adaptable MT evaluation models that exploit information from both the
source input and a target-language reference translation in order to more
accurately predict MT quality. To showcase our framework, we train three models
with different types of human judgements: Direct Assessments, Human-mediated
Translation Edit Rate and Multidimensional Quality Metrics. Our models achieve
new state-of-the-art performance on the WMT 2019 Metrics shared task and
demonstrate robustness to high-performing systems.
| 2,020 | Computation and Language |
Looking Beyond Sentence-Level Natural Language Inference for Downstream
Tasks | In recent years, the Natural Language Inference (NLI) task has garnered
significant attention, with new datasets and models achieving near human-level
performance on it. However, the full promise of NLI -- particularly that it
learns knowledge that should be generalizable to other downstream NLP tasks --
has not been realized. In this paper, we study this unfulfilled promise from
the lens of two downstream tasks: question answering (QA), and text
summarization. We conjecture that a key difference between the NLI datasets and
these downstream tasks concerns the length of the premise; and that creating
new long premise NLI datasets out of existing QA datasets is a promising avenue
for training a truly generalizable NLI model. We validate our conjecture by
showing competitive results on the task of QA and obtaining the best reported
results on the task of Checking Factual Correctness of Summaries.
| 2,020 | Computation and Language |
A Simple and Effective Self-Supervised Contrastive Learning Framework
for Aspect Detection | Unsupervised aspect detection (UAD) aims at automatically extracting
interpretable aspects and identifying aspect-specific segments (such as
sentences) from online reviews. However, recent deep learning-based topic
models, specifically aspect-based autoencoder, suffer from several problems,
such as extracting noisy aspects and poorly mapping aspects discovered by
models to the aspects of interest. To tackle these challenges, in this paper,
we first propose a self-supervised contrastive learning framework and an
attention-based model equipped with a novel smooth self-attention (SSA) module
for the UAD task in order to learn better representations for aspects and
review segments. Secondly, we introduce a high-resolution selective mapping
(HRSMap) method to efficiently assign aspects discovered by the model to
aspects of interest. We also propose using a knowledge distilling technique to
further improve the aspect detection performance. Our methods outperform
several recent unsupervised and weakly supervised approaches on publicly
available benchmark user review datasets. Aspect interpretation results show
that extracted aspects are meaningful, have good coverage, and can be easily
mapped to aspects of interest. Ablation studies and attention weight
visualization also demonstrate the effectiveness of SSA and the knowledge
distilling method.
| 2,021 | Computation and Language |
An Interpretable and Uncertainty Aware Multi-Task Framework for
Multi-Aspect Sentiment Analysis | In recent years, several online platforms have seen a rapid increase in the
number of review systems that request users to provide aspect-level feedback.
Document-level Multi-aspect Sentiment Classification (DMSC), where the goal is
to predict the ratings/sentiment from a review at an individual aspect level,
has become a challenging and imminent problem. To tackle this challenge, we
propose a deliberate self-attention-based deep neural network model, namely
FEDAR, for the DMSC problem, which can achieve competitive performance while
also being able to interpret the predictions made. FEDAR is equipped with a
highway word embedding layer to transfer knowledge from pre-trained word
embeddings, an RNN encoder layer with output features enriched by pooling and
factorization techniques, and a deliberate self-attention layer. In addition,
we also propose an Attention-driven Keywords Ranking (AKR) method, which can
automatically discover aspect keywords and aspect-level opinion keywords from
the review corpus based on the attention weights. These keywords are
significant for rating predictions by FEDAR. Since crowdsourcing annotation can
be an alternate way to recover missing ratings of reviews, we propose a
LEcture-AuDience (LEAD) strategy to estimate model uncertainty in the context
of multi-task learning, so that valuable human resources can focus on the most
uncertain predictions. Our extensive set of experiments on five different
open-domain DMSC datasets demonstrate the superiority of the proposed FEDAR and
LEAD models. We further introduce two new DMSC datasets in the healthcare
domain and benchmark different baseline models and our models on them.
Attention weights visualization results and visualization of aspect and opinion
keywords demonstrate the interpretability of our model and the effectiveness of
our AKR method.
| 2,021 | Computation and Language |
Tradeoffs in Sentence Selection Techniques for Open-Domain Question
Answering | Current methods in open-domain question answering (QA) usually employ a
pipeline of first retrieving relevant documents, then applying strong reading
comprehension (RC) models to that retrieved text. However, modern RC models are
complex and expensive to run, so techniques to prune the space of retrieved
text are critical to allow this approach to scale. In this paper, we focus on
approaches which apply an intermediate sentence selection step to address this
issue, and investigate the best practices for this approach. We describe two
groups of models for sentence selection: QA-based approaches, which run a
full-fledged QA system to identify answer candidates, and retrieval-based
models, which find parts of each passage specifically related to each question.
We examine trade-offs between processing speed and task performance in these
two approaches, and demonstrate an ensemble module that represents a hybrid of
the two. From experiments on Open-SQuAD and TriviaQA, we show that very
lightweight QA models can do well at this task, but retrieval-based models are
faster still. An ensemble module we describe balances between the two and
generalizes well cross-domain.
| 2,020 | Computation and Language |
Will it Unblend? | Natural language processing systems often struggle with out-of-vocabulary
(OOV) terms, which do not appear in training data. Blends, such as
"innoventor", are one particularly challenging class of OOV, as they are formed
by fusing together two or more bases that relate to the intended meaning in
unpredictable manners and degrees. In this work, we run experiments on a novel
dataset of English OOV blends to quantify the difficulty of interpreting the
meanings of blends by large-scale contextual language models such as BERT. We
first show that BERT's processing of these blends does not fully access the
component meanings, leaving their contextual representations semantically
impoverished. We find this is mostly due to the loss of characters resulting
from blend formation. Then, we assess how easily different models can recognize
the structure and recover the origin of blends, and find that context-aware
embedding systems outperform character-level and context-free embeddings,
although their results are still far from satisfactory.
| 2,020 | Computation and Language |
Computer Assisted Translation with Neural Quality Estimation and
Automatic Post-Editing | With the advent of neural machine translation, there has been a marked shift
towards leveraging and consuming the machine translation results. However, the
gap between machine translation systems and human translators needs to be
manually closed by post-editing. In this paper, we propose an end-to-end deep
learning framework of the quality estimation and automatic post-editing of the
machine translation output. Our goal is to provide error correction suggestions
and to further relieve the burden of human translators through an interpretable
model. To imitate the behavior of human translators, we design three efficient
delegation modules -- quality estimation, generative post-editing, and atomic
operation post-editing and construct a hierarchical model based on them. We
examine this approach with the English--German dataset from WMT 2017 APE shared
task and our experimental results can achieve the state-of-the-art performance.
We also verify that the certified translators can significantly expedite their
post-editing processing with our model in human evaluation.
| 2,020 | Computation and Language |
Long-Short Term Masking Transformer: A Simple but Effective Baseline for
Document-level Neural Machine Translation | Many document-level neural machine translation (NMT) systems have explored
the utility of context-aware architecture, usually requiring an increasing
number of parameters and computational complexity. However, few attention is
paid to the baseline model. In this paper, we research extensively the pros and
cons of the standard transformer in document-level translation, and find that
the auto-regressive property can simultaneously bring both the advantage of the
consistency and the disadvantage of error accumulation. Therefore, we propose a
surprisingly simple long-short term masking self-attention on top of the
standard transformer to both effectively capture the long-range dependence and
reduce the propagation of errors. We examine our approach on the two publicly
available document-level datasets. We can achieve a strong result in BLEU and
capture discourse phenomena.
| 2,020 | Computation and Language |
Prior Art Search and Reranking for Generated Patent Text | Generative models, such as GPT-2, have demonstrated impressive results
recently. A fundamental question we'd like to address is: where did the
generated text come from? This work is our initial effort toward answering the
question by using prior art search. The purpose of the prior art search is to
find the most similar prior text in the training data of GPT-2. We take a
reranking approach and apply it to the patent domain. Specifically, we
pre-train GPT-2 models from scratch by using the patent data from the USPTO.
The input for the prior art search is the patent text generated by the GPT-2
model. We also pre-trained BERT models from scratch for converting patent text
to embeddings. The steps of reranking are: (1) search the most similar text in
the training data of GPT-2 by taking a bag-of-word ranking approach (BM25), (2)
convert the search results in text format to BERT embeddings, and (3) provide
the final result by ranking the BERT embeddings based on their similarities
with the patent text generated by GPT-2. The experiments in this work show that
such reranking is better than ranking with embeddings alone. However, our mixed
results also indicate that calculating the semantic similarities among long
text spans is still challenging. To our knowledge, this work is the first to
implement a reranking system to identify retrospectively the most similar
inputs to a GPT model based on its output.
| 2,021 | Computation and Language |
Enhancing Dialogue Generation via Multi-Level Contrastive Learning | Most of the existing works for dialogue generation are data-driven models
trained directly on corpora crawled from websites. They mainly focus on
improving the model architecture to produce better responses but pay little
attention to considering the quality of the training data contrastively. In
this paper, we propose a multi-level contrastive learning paradigm to model the
fine-grained quality of the responses with respect to the query. A Rank-aware
Calibration (RC) network is designed to construct the multi-level contrastive
optimization objectives. Since these objectives are calculated based on the
sentence level, which may erroneously encourage/suppress the generation of
uninformative/informative words. To tackle this incidental issue, on one hand,
we design an exquisite token-level strategy for estimating the instance loss
more accurately. On the other hand, we build a Knowledge Inference (KI)
component to capture the keyword knowledge from the reference during training
and exploit such information to encourage the generation of informative words.
We evaluate the proposed model on a carefully annotated dialogue dataset and
the results suggest that our model can generate more relevant and diverse
responses compared to the baseline models.
| 2,021 | Computation and Language |
Weight Distillation: Transferring the Knowledge in Neural Network
Parameters | Knowledge distillation has been proven to be effective in model acceleration
and compression. It allows a small network to learn to generalize in the same
way as a large network. Recent successes in pre-training suggest the
effectiveness of transferring model parameters. Inspired by this, we
investigate methods of model acceleration and compression in another line of
research. We propose Weight Distillation to transfer the knowledge in the large
network parameters through a parameter generator. Our experiments on WMT16
En-Ro, NIST12 Zh-En, and WMT14 En-De machine translation tasks show that weight
distillation can train a small network that is 1.88~2.94x faster than the large
network but with competitive performance. With the same sized small network,
weight distillation can outperform knowledge distillation by 0.51~1.82 BLEU
points.
| 2,021 | Computation and Language |
CLEVR Parser: A Graph Parser Library for Geometric Learning on Language
Grounded Image Scenes | The CLEVR dataset has been used extensively in language grounded visual
reasoning in Machine Learning (ML) and Natural Language Processing (NLP)
domains. We present a graph parser library for CLEVR, that provides
functionalities for object-centric attributes and relationships extraction, and
construction of structural graph representations for dual modalities.
Structural order-invariant representations enable geometric learning and can
aid in downstream tasks like language grounding to vision, robotics,
compositionality, interpretability, and computational grammar construction. We
provide three extensible main components - parser, embedder, and visualizer
that can be tailored to suit specific learning setups. We also provide
out-of-the-box functionality for seamless integration with popular deep graph
neural network (GNN) libraries. Additionally, we discuss downstream usage and
applications of the library, and how it accelerates research for the NLP
research community.
| 2,020 | Computation and Language |
Extracting Summary Knowledge Graphs from Long Documents | Knowledge graphs capture entities and relations from long documents and can
facilitate reasoning in many downstream applications. Extracting compact
knowledge graphs containing only salient entities and relations is important
but challenging for understanding and summarizing long documents. We introduce
a new text-to-graph task of predicting summarized knowledge graphs from long
documents. We develop a dataset of 200k document/graph pairs using automatic
and human annotations. We also develop strong baselines for this task based on
graph learning and text summarization, and provide quantitative and qualitative
studies of their effect.
| 2,021 | Computation and Language |
Nominal Compound Chain Extraction: A New Task for Semantic-enriched
Lexical Chain | Lexical chain consists of cohesion words in a document, which implies the
underlying structure of a text, and thus facilitates downstream NLP tasks.
Nevertheless, existing work focuses on detecting the simple surface lexicons
with shallow syntax associations, ignoring the semantic-aware lexical compounds
as well as the latent semantic frames, (e.g., topic), which can be much more
crucial for real-world NLP applications. In this paper, we introduce a novel
task, Nominal Compound Chain Extraction (NCCE), extracting and clustering all
the nominal compounds that share identical semantic topics. In addition, we
model the task as a two-stage prediction (i.e., compound extraction and chain
detection), which is handled via a proposed joint framework. The model employs
the BERT encoder to yield contextualized document representation. Also, HowNet
is exploited as external resources for offering rich sememe information. The
experiments are based on our manually annotated corpus, and the results prove
the necessity of the NCCE task as well as the effectiveness of our joint
approach.
| 2,020 | Computation and Language |
Aggressive Language Detection with Joint Text Normalization via
Adversarial Multi-task Learning | Aggressive language detection (ALD), detecting the abusive and offensive
language in texts, is one of the crucial applications in NLP community. Most
existing works treat ALD as regular classification with neural models, while
ignoring the inherent conflicts of social media text that they are quite
unnormalized and irregular. In this work, we target improving the ALD by
jointly performing text normalization (TN), via an adversarial multi-task
learning framework. The private encoders for ALD and TN focus on the
task-specific features retrieving, respectively, and the shared encoder learns
the underlying common features over two tasks. During adversarial training, a
task discriminator distinguishes the separate learning of ALD or TN.
Experimental results on four ALD datasets show that our model outperforms all
baselines under differing settings by large margins, demonstrating the
necessity of joint learning the TN with ALD. Further analysis is conducted for
a better understanding of our method.
| 2,020 | Computation and Language |
OpenAttack: An Open-source Textual Adversarial Attack Toolkit | Textual adversarial attacking has received wide and increasing attention in
recent years. Various attack models have been proposed, which are enormously
distinct and implemented with different programming frameworks and settings.
These facts hinder quick utilization and fair comparison of attack models. In
this paper, we present an open-source textual adversarial attack toolkit named
OpenAttack to solve these issues. Compared with existing other textual
adversarial attack toolkits, OpenAttack has its unique strengths in support for
all attack types, multilinguality, and parallel processing. Currently,
OpenAttack includes 15 typical attack models that cover all attack types. Its
highly inclusive modular design not only supports quick utilization of existing
attack models, but also enables great flexibility and extensibility. OpenAttack
has broad uses including comparing and evaluating attack models, measuring
robustness of a model, assisting in developing new attack models, and
adversarial training. Source code and documentation can be obtained at
https://github.com/thunlp/OpenAttack.
| 2,021 | Computation and Language |
Learning to Attack: Towards Textual Adversarial Attacking in Real-world
Situations | Adversarial attacking aims to fool deep neural networks with adversarial
examples. In the field of natural language processing, various textual
adversarial attack models have been proposed, varying in the accessibility to
the victim model. Among them, the attack models that only require the output of
the victim model are more fit for real-world situations of adversarial
attacking. However, to achieve high attack performance, these models usually
need to query the victim model too many times, which is neither efficient nor
viable in practice. To tackle this problem, we propose a reinforcement learning
based attack model, which can learn from attack history and launch attacks more
efficiently. In experiments, we evaluate our model by attacking several
state-of-the-art models on the benchmark datasets of multiple tasks including
sentiment analysis, text classification and natural language inference.
Experimental results demonstrate that our model consistently achieves both
better attack performance and higher efficiency than recently proposed baseline
methods. We also find our attack model can bring more robustness improvement to
the victim model by adversarial training. All the code and data of this paper
will be made public.
| 2,020 | Computation and Language |
BioALBERT: A Simple and Effective Pre-trained Language Model for
Biomedical Named Entity Recognition | In recent years, with the growing amount of biomedical documents, coupled
with advancement in natural language processing algorithms, the research on
biomedical named entity recognition (BioNER) has increased exponentially.
However, BioNER research is challenging as NER in the biomedical domain are:
(i) often restricted due to limited amount of training data, (ii) an entity can
refer to multiple types and concepts depending on its context and, (iii) heavy
reliance on acronyms that are sub-domain specific. Existing BioNER approaches
often neglect these issues and directly adopt the state-of-the-art (SOTA)
models trained in general corpora which often yields unsatisfactory results. We
propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from
Transformers for Biomedical Text Mining) bioALBERT, an effective
domain-specific language model trained on large-scale biomedical corpora
designed to capture biomedical context-dependent NER. We adopted a
self-supervised loss used in ALBERT that focuses on modelling inter-sentence
coherence to better learn context-dependent representations and incorporated
parameter reduction techniques to lower memory consumption and increase the
training speed in BioNER. In our experiments, BioALBERT outperformed
comparative SOTA BioNER models on eight biomedical NER benchmark datasets with
four different entity types. We trained four different variants of BioALBERT
models which are available for the research community to be used in future
research.
| 2,020 | Computation and Language |
Word class flexibility: A deep contextualized approach | Word class flexibility refers to the phenomenon whereby a single word form is
used across different grammatical categories. Extensive work in linguistic
typology has sought to characterize word class flexibility across languages,
but quantifying this phenomenon accurately and at scale has been fraught with
difficulties. We propose a principled methodology to explore regularity in word
class flexibility. Our method builds on recent work in contextualized word
embeddings to quantify semantic shift between word classes (e.g., noun-to-verb,
verb-to-noun), and we apply this method to 37 languages. We find that
contextualized embeddings not only capture human judgment of class variation
within words in English, but also uncover shared tendencies in class
flexibility across languages. Specifically, we find greater semantic variation
when flexible lemmas are used in their dominant word class, supporting the view
that word class flexibility is a directional process. Our work highlights the
utility of deep contextualized models in linguistic typology.
| 2,020 | Computation and Language |
Towards Computational Linguistics in Minangkabau Language: Studies on
Sentiment Analysis and Machine Translation | Although some linguists (Rusmali et al., 1985; Crouch, 2009) have fairly
attempted to define the morphology and syntax of Minangkabau, information
processing in this language is still absent due to the scarcity of the
annotated resource. In this work, we release two Minangkabau corpora: sentiment
analysis and machine translation that are harvested and constructed from
Twitter and Wikipedia. We conduct the first computational linguistics in
Minangkabau language employing classic machine learning and
sequence-to-sequence models such as LSTM and Transformer. Our first experiments
show that the classification performance over Minangkabau text significantly
drops when tested with the model trained in Indonesian. Whereas, in the machine
translation experiment, a simple word-to-word translation using a bilingual
dictionary outperforms LSTM and Transformer model in terms of BLEU score.
| 2,020 | Computation and Language |
Biomedical Event Extraction with Hierarchical Knowledge Graphs | Biomedical event extraction is critical in understanding biomolecular
interactions described in scientific corpus. One of the main challenges is to
identify nested structured events that are associated with non-indicative
trigger words. We propose to incorporate domain knowledge from Unified Medical
Language System (UMLS) to a pre-trained language model via Graph
Edge-conditioned Attention Networks (GEANet) and hierarchical graph
representation. To better recognize the trigger words, each sentence is first
grounded to a sentence graph based on a jointly modeled hierarchical knowledge
graph from UMLS. The grounded graphs are then propagated by GEANet, a novel
graph neural networks for enhanced capabilities in inferring complex events. On
BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and
3.19% F1 improvements on all events and complex events, respectively. Ablation
studies confirm the importance of GEANet and hierarchical KG.
| 2,020 | Computation and Language |
Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New
Datasets for Bengali-English Machine Translation | Despite being the seventh most widely spoken language in the world, Bengali
has received much less attention in machine translation literature due to being
low in resources. Most publicly available parallel corpora for Bengali are not
large enough; and have rather poor quality, mostly because of incorrect
sentence alignments resulting from erroneous sentence segmentation, and also
because of a high volume of noise present in them. In this work, we build a
customized sentence segmenter for Bengali and propose two novel methods for
parallel corpus creation on low-resource setups: aligner ensembling and batch
filtering. With the segmenter and the two methods combined, we compile a
high-quality Bengali-English parallel corpus comprising of 2.75 million
sentence pairs, more than 2 million of which were not available before.
Training on neural models, we achieve an improvement of more than 9 BLEU score
over previous approaches to Bengali-English machine translation. We also
evaluate on a new test set of 1000 pairs made with extensive quality control.
We release the segmenter, parallel corpus, and the evaluation set, thus
elevating Bengali from its low-resource status. To the best of our knowledge,
this is the first ever large scale study on Bengali-English machine
translation. We believe our study will pave the way for future research on
Bengali-English machine translation as well as other low-resource languages.
Our data and code are available at https://github.com/csebuetnlp/banglanmt.
| 2,020 | Computation and Language |
Understanding Mention Detector-Linker Interaction in Neural Coreference
Resolution | Despite significant recent progress in coreference resolution, the quality of
current state-of-the-art systems still considerably trails behind human-level
performance. Using the CoNLL-2012 and PreCo datasets, we dissect the best
instantiation of the mainstream end-to-end coreference resolution model that
underlies most current best-performing coreference systems, and empirically
analyze the behavior of its two components: mention detector and mention
linker. While the detector traditionally focuses heavily on recall as a design
decision, we demonstrate the importance of precision, calling for their
balance. However, we point out the difficulty in building a precise detector
due to its inability to make important anaphoricity decisions. We also
highlight the enormous room for improving the linker and show that the rest of
its errors mainly involve pronoun resolution. We propose promising next steps
and hope our findings will help future research in coreference resolution.
| 2,021 | Computation and Language |
Softmax Tempering for Training Neural Machine Translation Models | Neural machine translation (NMT) models are typically trained using a softmax
cross-entropy loss where the softmax distribution is compared against smoothed
gold labels. In low-resource scenarios, NMT models tend to over-fit because the
softmax distribution quickly approaches the gold label distribution. To address
this issue, we propose to divide the logits by a temperature coefficient, prior
to applying softmax, during training. In our experiments on 11 language pairs
in the Asian Language Treebank dataset and the WMT 2019 English-to-German
translation task, we observed significant improvements in translation quality
by up to 3.9 BLEU points. Furthermore, softmax tempering makes the greedy
search to be as good as beam search decoding in terms of translation quality,
enabling 1.5 to 3.5 times speed-up. We also study the impact of softmax
tempering on multilingual NMT and recurrently stacked NMT, both of which aim to
reduce the NMT model size by parameter sharing thereby verifying the utility of
temperature in developing compact NMT models. Finally, an analysis of softmax
entropies and gradients reveal the impact of our method on the internal
behavior of NMT models.
| 2,020 | Computation and Language |
Difference-aware Knowledge Selection for Knowledge-grounded Conversation
Generation | In a multi-turn knowledge-grounded dialog, the difference between the
knowledge selected at different turns usually provides potential clues to
knowledge selection, which has been largely neglected in previous research. In
this paper, we propose a difference-aware knowledge selection method. It first
computes the difference between the candidate knowledge sentences provided at
the current turn and those chosen in the previous turns. Then, the differential
information is fused with or disentangled from the contextual information to
facilitate final knowledge selection. Automatic, human observational, and
interactive evaluation shows that our method is able to select knowledge more
accurately and generate more informative responses, significantly outperforming
the state-of-the-art baselines. The codes are available at
https://github.com/chujiezheng/DiffKS.
| 2,020 | Computation and Language |
F^2-Softmax: Diversifying Neural Text Generation via Frequency
Factorized Softmax | Despite recent advances in neural text generation, encoding the rich
diversity in human language remains elusive. We argue that the sub-optimal text
generation is mainly attributable to the imbalanced token distribution, which
particularly misdirects the learning model when trained with the
maximum-likelihood objective. As a simple yet effective remedy, we propose two
novel methods, F^2-Softmax and MefMax, for a balanced training even with the
skewed frequency distribution. MefMax assigns tokens uniquely to frequency
classes, trying to group tokens with similar frequencies and equalize frequency
mass between the classes. F^2-Softmax then decomposes a probability
distribution of the target token into a product of two conditional
probabilities of (i) frequency class, and (ii) token from the target frequency
class. Models learn more uniform probability distributions because they are
confined to subsets of vocabularies. Significant performance gains on seven
relevant metrics suggest the supremacy of our approach in improving not only
the diversity but also the quality of generated texts.
| 2,020 | Computation and Language |
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired
Data | Recent advances in open-domain dialogue systems rely on the success of neural
models that are trained on large-scale data. However, collecting large-scale
dialogue data is usually time-consuming and labor-intensive. To address this
data dilemma, we propose a novel data augmentation method for training
open-domain dialogue models by utilizing unpaired data. Specifically, a
data-level distillation process is first proposed to construct augmented
dialogues where both post and response are retrieved from the unpaired data. A
ranking module is employed to filter out low-quality dialogues. Further, a
model-level distillation process is employed to distill a teacher model trained
on high-quality paired data to augmented dialogue pairs, thereby preventing
dialogue models from being affected by the noise in the augmented data.
Automatic and manual evaluation indicates that our method can produce
high-quality dialogue pairs with diverse contents, and the proposed data-level
and model-level dialogue distillation can improve the performance of
competitive baselines.
| 2,020 | Computation and Language |
Persian Ezafe Recognition Using Transformers and Its Role in
Part-Of-Speech Tagging | Ezafe is a grammatical particle in some Iranian languages that links two
words together. Regardless of the important information it conveys, it is
almost always not indicated in Persian script, resulting in mistakes in reading
complex sentences and errors in natural language processing tasks. In this
paper, we experiment with different machine learning methods to achieve
state-of-the-art results in the task of ezafe recognition. Transformer-based
methods, BERT and XLMRoBERTa, achieve the best results, the latter achieving
2.68% F1-score more than the previous state-of-the-art. We, moreover, use ezafe
information to improve Persian part-of-speech tagging results and show that
such information will not be useful to transformer-based methods and explain
why that might be the case.
| 2,020 | Computation and Language |
Relation Extraction from Biomedical and Clinical Text: Unified Multitask
Learning Framework | To minimize the accelerating amount of time invested in the biomedical
literature search, numerous approaches for automated knowledge extraction have
been proposed. Relation extraction is one such task where semantic relations
between the entities are identified from the free text. In the biomedical
domain, extraction of regulatory pathways, metabolic processes, adverse drug
reaction or disease models necessitates knowledge from the individual
relations, for example, physical or regulatory interactions between genes,
proteins, drugs, chemical, disease or phenotype. In this paper, we study the
relation extraction task from three major biomedical and clinical tasks, namely
drug-drug interaction, protein-protein interaction, and medical concept
relation extraction. Towards this, we model the relation extraction problem in
multi-task learning (MTL) framework and introduce for the first time the
concept of structured self-attentive network complemented with the adversarial
learning approach for the prediction of relationships from the biomedical and
clinical text. The fundamental notion of MTL is to simultaneously learn
multiple problems together by utilizing the concepts of the shared
representation. Additionally, we also generate the highly efficient single task
model which exploits the shortest dependency path embedding learned over the
attentive gated recurrent unit to compare our proposed MTL models. The
framework we propose significantly improves overall the baselines (deep
learning techniques) and single-task models for predicting the relationships,
without compromising on the performance of all the tasks.
| 2,020 | Computation and Language |
Vector Projection Network for Few-shot Slot Tagging in Natural Language
Understanding | Few-shot slot tagging becomes appealing for rapid domain transfer and
adaptation, motivated by the tremendous development of conversational dialogue
systems. In this paper, we propose a vector projection network for few-shot
slot tagging, which exploits projections of contextual word embeddings on each
target label vector as word-label similarities. Essentially, this approach is
equivalent to a normalized linear model with an adaptive bias. The contrastive
experiment demonstrates that our proposed vector projection based similarity
metric can significantly surpass other variants. Specifically, in the five-shot
setting on benchmarks SNIPS and NER, our method outperforms the strongest
few-shot learning baseline by $6.30$ and $13.79$ points on F$_1$ score,
respectively. Our code will be released at
https://github.com/sz128/few_shot_slot_tagging_and_NER.
| 2,020 | Computation and Language |
Improving Robustness and Generality of NLP Models Using Disentangled
Representations | Supervised neural networks, which first map an input $x$ to a single
representation $z$, and then map $z$ to the output label $y$, have achieved
remarkable success in a wide range of natural language processing (NLP) tasks.
Despite their success, neural models lack for both robustness and generality:
small perturbations to inputs can result in absolutely different outputs; the
performance of a model trained on one domain drops drastically when tested on
another domain.
In this paper, we present methods to improve robustness and generality of NLP
models from the standpoint of disentangled representation learning. Instead of
mapping $x$ to a single representation $z$, the proposed strategy maps $x$ to a
set of representations $\{z_1,z_2,...,z_K\}$ while forcing them to be
disentangled. These representations are then mapped to different logits $l$s,
the ensemble of which is used to make the final prediction $y$. We propose
different methods to incorporate this idea into currently widely-used models,
including adding an $L$2 regularizer on $z$s or adding Total Correlation (TC)
under the framework of variational information bottleneck (VIB). We show that
models trained with the proposed criteria provide better robustness and domain
adaptation ability in a wide range of supervised learning tasks.
| 2,020 | Computation and Language |
Assessing the Severity of Health States based on Social Media Posts | The unprecedented growth of Internet users has resulted in an abundance of
unstructured information on social media including health forums, where
patients request health-related information or opinions from other users.
Previous studies have shown that online peer support has limited effectiveness
without expert intervention. Therefore, a system capable of assessing the
severity of health state from the patients' social media posts can help health
professionals (HP) in prioritizing the user's post. In this study, we inspect
the efficacy of different aspects of Natural Language Understanding (NLU) to
identify the severity of the user's health state in relation to two
perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate,
Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse
Effect, Other) in online health communities. We propose a multiview learning
framework that models both the textual content as well as
contextual-information to assess the severity of the user's health state.
Specifically, our model utilizes the NLU views such as sentiment, emotions,
personality, and use of figurative language to extract the contextual
information. The diverse NLU views demonstrate its effectiveness on both the
tasks and as well as on the individual disease to assess a user's health.
| 2,020 | Computation and Language |
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization
in News Media | In this paper we suggest a minimally-supervised approach for identifying
nuanced frames in news article coverage of politically divisive topics. We
suggest to break the broad policy frames suggested by Boydstun et al., 2014
into fine-grained subframes which can capture differences in political ideology
in a better way. We evaluate the suggested subframes and their embedding,
learned using minimal supervision, over three topics, namely, immigration,
gun-control and abortion. We demonstrate the ability of the subframes to
capture ideological differences and analyze political discourse in news media.
| 2,020 | Computation and Language |
Modality-Transferable Emotion Embeddings for Low-Resource Multimodal
Emotion Recognition | Despite the recent achievements made in the multi-modal emotion recognition
task, two problems still exist and have not been well investigated: 1) the
relationship between different emotion categories are not utilized, which leads
to sub-optimal performance; and 2) current models fail to cope well with
low-resource emotions, especially for unseen emotions. In this paper, we
propose a modality-transferable model with emotion embeddings to tackle the
aforementioned issues. We use pre-trained word embeddings to represent emotion
categories for textual data. Then, two mapping functions are learned to
transfer these embeddings into visual and acoustic spaces. For each modality,
the model calculates the representation distance between the input sequence and
target emotions and makes predictions based on the distances. By doing so, our
model can directly adapt to the unseen emotions in any modality since we have
their pre-trained embeddings and modality mapping functions. Experiments show
that our model achieves state-of-the-art performance on most of the emotion
categories. In addition, our model also outperforms existing baselines in the
zero-shot and few-shot scenarios for unseen emotions.
| 2,020 | Computation and Language |
Generative Imagination Elevates Machine Translation | There are common semantics shared across text and images. Given a sentence in
a source language, whether depicting the visual scene helps translation into a
target language? Existing multimodal neural machine translation methods (MNMT)
require triplets of bilingual sentence - image for training and tuples of
source sentence - image for inference. In this paper, we propose ImagiT, a
novel machine translation method via visual imagination. ImagiT first learns to
generate visual representation from the source sentence, and then utilizes both
source sentence and the "imagined representation" to produce a target
translation. Unlike previous methods, it only needs the source sentence at the
inference time. Experiments demonstrate that ImagiT benefits from visual
imagination and significantly outperforms the text-only neural machine
translation baselines. Further analysis reveals that the imagination process in
ImagiT helps fill in missing information when performing the degradation
strategy.
| 2,021 | Computation and Language |
Alleviating the Inequality of Attention Heads for Neural Machine
Translation | Recent studies show that the attention heads in Transformer are not equal. We
relate this phenomenon to the imbalance training of multi-head attention and
the model dependence on specific heads. To tackle this problem, we propose a
simple masking method: HeadMask, in two specific ways. Experiments show that
translation improvements are achieved on multiple language pairs. Subsequent
empirical analyses also support our assumption and confirm the effectiveness of
the method.
| 2,022 | Computation and Language |
Accent Estimation of Japanese Words from Their Surfaces and
Romanizations for Building Large Vocabulary Accent Dictionaries | In Japanese text-to-speech (TTS), it is necessary to add accent information
to the input sentence. However, there are a limited number of publicly
available accent dictionaries, and those dictionaries e.g. UniDic, do not
contain many compound words, proper nouns, etc., which are required in a
practical TTS system. In order to build a large scale accent dictionary that
contains those words, the authors developed an accent estimation technique that
predicts the accent of a word from its limited information, namely the surface
(e.g. kanji) and the yomi (simplified phonetic information). It is
experimentally shown that the technique can estimate accents with high
accuracies, especially for some categories of words. The authors applied this
technique to an existing large vocabulary Japanese dictionary NEologd, and
obtained a large vocabulary Japanese accent dictionary. Many cases have been
observed in which the use of this dictionary yields more appropriate phonetic
information than UniDic.
| 2,020 | Computation and Language |
Profile Consistency Identification for Open-domain Dialogue Agents | Maintaining a consistent attribute profile is crucial for dialogue agents to
naturally converse with humans. Existing studies on improving attribute
consistency mainly explored how to incorporate attribute information in the
responses, but few efforts have been made to identify the consistency relations
between response and attribute profile. To facilitate the study of profile
consistency identification, we create a large-scale human-annotated dataset
with over 110K single-turn conversations and their key-value attribute
profiles. Explicit relation between response and profile is manually labeled.
We also propose a key-value structure information enriched BERT model to
identify the profile consistency, and it gained improvements over strong
baselines. Further evaluations on downstream tasks demonstrate that the profile
consistency identification model is conducive for improving dialogue
consistency.
| 2,021 | Computation and Language |
"Listen, Understand and Translate": Triple Supervision Decouples
End-to-end Speech-to-text Translation | An end-to-end speech-to-text translation (ST) takes audio in a source
language and outputs the text in a target language. Existing methods are
limited by the amount of parallel corpus. Can we build a system to fully
utilize signals in a parallel ST corpus? We are inspired by human understanding
system which is composed of auditory perception and cognitive processing. In
this paper, we propose Listen-Understand-Translate, (LUT), a unified framework
with triple supervision signals to decouple the end-to-end speech-to-text
translation task. LUT is able to guide the acoustic encoder to extract as much
information from the auditory input. In addition, LUT utilizes a pre-trained
BERT model to enforce the upper encoder to produce as much semantic information
as possible, without extra data. We perform experiments on a diverse set of
speech translation benchmarks, including Librispeech English-French, IWSLT
English-German and TED English-Chinese. Our results demonstrate LUT achieves
the state-of-the-art performance, outperforming previous methods. The code is
available at https://github.com/dqqcasia/st.
| 2,021 | Computation and Language |
Knowledge Bridging for Empathetic Dialogue Generation | Lack of external knowledge makes empathetic dialogue systems difficult to
perceive implicit emotions and learn emotional interactions from limited
dialogue history. To address the above problems, we propose to leverage
external knowledge, including commonsense knowledge and emotional lexical
knowledge, to explicitly understand and express emotions in empathetic dialogue
generation. We first enrich the dialogue history by jointly interacting with
external knowledge and construct an emotional context graph. Then we learn
emotional context representations from the knowledge-enriched emotional context
graph and distill emotional signals, which are the prerequisites to predicate
emotions expressed in responses. Finally, to generate the empathetic response,
we propose an emotional cross-attention mechanism to learn the emotional
dependencies from the emotional context graph. Extensive experiments conducted
on a benchmark dataset verify the effectiveness of the proposed method. In
addition, we find the performance of our method can be further improved by
integrating with a pre-trained model that works orthogonally.
| 2,021 | Computation and Language |
Multitask Pointer Network for Multi-Representational Parsing | We propose a transition-based approach that, by training a single model, can
efficiently parse any input sentence with both constituent and dependency
trees, supporting both continuous/projective and discontinuous/non-projective
syntactic structures. To that end, we develop a Pointer Network architecture
with two separate task-specific decoders and a common encoder, and follow a
multitask learning strategy to jointly train them. The resulting quadratic
system, not only becomes the first parser that can jointly produce both
unrestricted constituent and dependency trees from a single model, but also
proves that both syntactic formalisms can benefit from each other during
training, achieving state-of-the-art accuracies in several widely-used
benchmarks such as the continuous English and Chinese Penn Treebanks, as well
as the discontinuous German NEGRA and TIGER datasets.
| 2,022 | Computation and Language |
Consecutive Decoding for Speech-to-text Translation | Speech-to-text translation (ST), which directly translates the source
language speech to the target language text, has attracted intensive attention
recently. However, the combination of speech recognition and machine
translation in a single model poses a heavy burden on the direct cross-modal
cross-lingual mapping. To reduce the learning difficulty, we propose
COnSecutive Transcription and Translation (COSTT), an integral approach for
speech-to-text translation. The key idea is to generate source transcript and
target translation text with a single decoder. It benefits the model training
so that additional large parallel text corpus can be fully exploited to enhance
the speech translation training. Our method is verified on three mainstream
datasets, including Augmented LibriSpeech English-French dataset, IWSLT2018
English-German dataset, and TED English-Chinese dataset. Experiments show that
our proposed COSTT outperforms or on par with the previous state-of-the-art
methods on the three datasets. We have released our code at
\url{https://github.com/dqqcasia/st}.
| 2,022 | Computation and Language |
Rethinking Supervised Learning and Reinforcement Learning in
Task-Oriented Dialogue Systems | Dialogue policy learning for task-oriented dialogue systems has enjoyed great
progress recently mostly through employing reinforcement learning methods.
However, these approaches have become very sophisticated. It is time to
re-evaluate it. Are we really making progress developing dialogue agents only
based on reinforcement learning? We demonstrate how (1)~traditional supervised
learning together with (2)~a simulator-free adversarial learning method can be
used to achieve performance comparable to state-of-the-art RL-based methods.
First, we introduce a simple dialogue action decoder to predict the appropriate
actions. Then, the traditional multi-label classification solution for dialogue
policy learning is extended by adding dense layers to improve the dialogue
agent performance. Finally, we employ the Gumbel-Softmax estimator to
alternatively train the dialogue agent and the dialogue reward model without
using reinforcement learning. Based on our extensive experimentation, we can
conclude the proposed methods can achieve more stable and higher performance
with fewer efforts, such as the domain knowledge required to design a user
simulator and the intractable parameter tuning in reinforcement learning. Our
main goal is not to beat reinforcement learning with supervised learning, but
to demonstrate the value of rethinking the role of reinforcement learning and
supervised learning in optimizing task-oriented dialogue systems.
| 2,020 | Computation and Language |
Content Planning for Neural Story Generation with Aristotelian Rescoring | Long-form narrative text generated from large language models manages a
fluent impersonation of human writing, but only at the local sentence level,
and lacks structure or global cohesion. We posit that many of the problems of
story generation can be addressed via high-quality content planning, and
present a system that focuses on how to learn good plot structures to guide
story generation. We utilize a plot-generation language model along with an
ensemble of rescoring models that each implement an aspect of good
story-writing as detailed in Aristotle's Poetics. We find that stories written
with our more principled plot-structure are both more relevant to a given
prompt and higher quality than baselines that do not content plan, or that plan
in an unprincipled way.
| 2,020 | Computation and Language |
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using
Transformers | In this paper, we describe our system submitted for SemEval 2020 Task 9,
Sentiment Analysis for Code-Mixed Social Media Text alongside other
experiments. Our best performing system is a Transfer Learning-based model that
fine-tunes "XLM-RoBERTa", a transformer-based multilingual masked language
model, on monolingual English and Spanish data and Spanish-English code-mixed
data. Our system outperforms the official task baseline by achieving a 70.1%
average F1-Score on the official leaderboard using the test set. For later
submissions, our system manages to achieve a 75.9% average F1-Score on the test
set using CodaLab username "ahmed0sultan".
| 2,020 | Computation and Language |
Adjusting for Confounders with Text: Challenges and an Empirical
Evaluation Framework for Causal Inference | Causal inference studies using textual social media data can provide
actionable insights on human behavior. Making accurate causal inferences with
text requires controlling for confounding which could otherwise impart bias.
Recently, many different methods for adjusting for confounders have been
proposed, and we show that these existing methods disagree with one another on
two datasets inspired by previous social media studies. Evaluating causal
methods is challenging, as ground truth counterfactuals are almost never
available. Presently, no empirical evaluation framework for causal methods
using text exists, and as such, practitioners must select their methods without
guidance. We contribute the first such framework, which consists of five tasks
drawn from real world studies. Our framework enables the evaluation of any
casual inference method using text. Across 648 experiments and two datasets, we
evaluate every commonly used causal inference method and identify their
strengths and weaknesses to inform social media researchers seeking to use such
methods, and guide future improvements. We make all tasks, data, and models
public to inform applications and encourage additional research.
| 2,022 | Computation and Language |
UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19
Event Extraction on Social Media | In this paper, we describe our approach in the shared task: COVID-19 event
extraction from Twitter. The objective of this task is to extract answers from
COVID-related tweets to a set of predefined slot-filling questions. Our
approach treats the event extraction task as a question answering task by
leveraging the transformer-based T5 text-to-text model.
According to the official evaluation scores returned, namely F1, our
submitted run achieves competitive performance compared to other participating
runs (Top 3). However, we argue that this evaluation may underestimate the
actual performance of runs based on text-generation. Although some such runs
may answer the slot questions well, they may not be an exact string match for
the gold standard answers. To measure the extent of this underestimation, we
adopt a simple exact-answer transformation method aiming at converting the
well-answered predictions to exactly-matched predictions. The results show that
after this transformation our run overall reaches the same level of performance
as the best participating run and state-of-the-art F1 scores in three of five
COVID-related events. Our code is publicly available to aid reproducibility
| 2,021 | Computation and Language |
Latin BERT: A Contextual Language Model for Classical Philology | We present Latin BERT, a contextual language model for the Latin language,
trained on 642.7 million words from a variety of sources spanning the Classical
era to the 21st century. In a series of case studies, we illustrate the
affordances of this language-specific model both for work in natural language
processing for Latin and in using computational methods for traditional
scholarship: we show that Latin BERT achieves a new state of the art for
part-of-speech tagging on all three Universal Dependency datasets for Latin and
can be used for predicting missing text (including critical emendations); we
create a new dataset for assessing word sense disambiguation for Latin and
demonstrate that Latin BERT outperforms static word embeddings; and we show
that it can be used for semantically-informed search by querying contextual
nearest neighbors. We publicly release trained models to help drive future work
in this space.
| 2,020 | Computation and Language |
Composed Variational Natural Language Generation for Few-shot Intents | In this paper, we focus on generating training examples for few-shot intents
in the realistic imbalanced scenario. To build connections between existing
many-shot intents and few-shot intents, we consider an intent as a combination
of a domain and an action, and propose a composed variational natural language
generator (CLANG), a transformer-based conditional variational autoencoder.
CLANG utilizes two latent variables to represent the utterances corresponding
to two different independent parts (domain and action) in the intent, and the
latent variables are composed together to generate natural examples.
Additionally, to improve the generator learning, we adopt the contrastive
regularization loss that contrasts the in-class with the out-of-class utterance
generation given the intent. To evaluate the quality of the generated
utterances, experiments are conducted on the generalized few-shot intent
detection task. Empirical results show that our proposed model achieves
state-of-the-art performances on two real-world intent detection datasets.
| 2,020 | Computation and Language |
"When they say weed causes depression, but it's your fav
antidepressant": Knowledge-aware Attention Framework for Relationship
Extraction | With the increasing legalization of medical and recreational use of cannabis,
more research is needed to understand the association between depression and
consumer behavior related to cannabis consumption. Big social media data has
potential to provide deeper insights about these associations to public health
analysts. In this interdisciplinary study, we demonstrate the value of
incorporating domain-specific knowledge in the learning process to identify the
relationships between cannabis use and depression. We develop an end-to-end
knowledge infused deep learning framework (Gated-K-BERT) that leverages the
pre-trained BERT language representation model and domain-specific declarative
knowledge source (Drug Abuse Ontology (DAO)) to jointly extract entities and
their relationship using gated fusion sharing mechanism. Our model is further
tailored to provide more focus to the entities mention in the sentence through
entity-position aware attention layer, where ontology is used to locate the
target entities position. Experimental results show that inclusion of the
knowledge-aware attentive representation in association with BERT can extract
the cannabis-depression relationship with better coverage in comparison to the
state-of-the-art relation extractor.
| 2,021 | Computation and Language |
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving
Out-of-Domain Robustness | Models that perform well on a training domain often fail to generalize to
out-of-domain (OOD) examples. Data augmentation is a common method used to
prevent overfitting and improve OOD generalization. However, in natural
language, it is difficult to generate new examples that stay on the underlying
data manifold. We introduce SSMBA, a data augmentation method for generating
synthetic training examples by using a pair of corruption and reconstruction
functions to move randomly on a data manifold. We investigate the use of SSMBA
in the natural language domain, leveraging the manifold assumption to
reconstruct corrupted text with masked language models. In experiments on
robustness benchmarks across 3 tasks and 9 datasets, SSMBA consistently
outperforms existing data augmentation methods and baseline models on both
in-domain and OOD data, achieving gains of 0.8% accuracy on OOD Amazon reviews,
1.8% accuracy on OOD MNLI, and 1.4 BLEU on in-domain IWSLT14 German-English.
| 2,020 | Computation and Language |
The Persian Dependency Treebank Made Universal | We describe an automatic method for converting the Persian Dependency
Treebank (Rasooli et al, 2013) to Universal Dependencies. This treebank
contains 29107 sentences. Our experiments along with manual linguistic analysis
show that our data is more compatible with Universal Dependencies than the
Uppsala Persian Universal Dependency Treebank (Seraji et al., 2016), and is
larger in size and more diverse in vocabulary. Our data brings in a labeled
attachment F-score of 85.2 in supervised parsing. Our delexicalized
Persian-to-English parser transfer experiments show that a parsing model
trained on our data is ~2% absolutely more accurate than that of Seraji et al.
(2016) in terms of labeled attachment score.
| 2,020 | Computation and Language |
An Empirical Study on Neural Keyphrase Generation | Recent years have seen a flourishing of neural keyphrase generation (KPG)
works, including the release of several large-scale datasets and a host of new
models to tackle them. Model performance on KPG tasks has increased
significantly with evolving deep learning research. However, there lacks a
comprehensive comparison among different model designs, and a thorough
investigation on related factors that may affect a KPG system's generalization
performance. In this empirical study, we aim to fill this gap by providing
extensive experimental results and analyzing the most crucial factors impacting
the generalizability of KPG models. We hope this study can help clarify some of
the uncertainties surrounding the KPG task and facilitate future research on
this topic.
| 2,021 | Computation and Language |
ALICE: Active Learning with Contrastive Natural Language Explanations | Training a supervised neural network classifier typically requires many
annotated training samples. Collecting and annotating a large number of data
points are costly and sometimes even infeasible. Traditional annotation process
uses a low-bandwidth human-machine communication interface: classification
labels, each of which only provides several bits of information. We propose
Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop
training framework that utilizes contrastive natural language explanations to
improve data efficiency in learning. ALICE learns to first use active learning
to select the most informative pairs of label classes to elicit contrastive
natural language explanations from experts. Then it extracts knowledge from
these explanations using a semantic parser. Finally, it incorporates the
extracted knowledge through dynamically changing the learning model's
structure. We applied ALICE in two visual recognition tasks, bird species
classification and social relationship classification. We found by
incorporating contrastive explanations, our models outperform baseline models
that are trained with 40-100% more training data. We found that adding 1
explanation leads to similar performance gain as adding 13-30 labeled training
data points.
| 2,020 | Computation and Language |
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