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What Can We Do to Improve Peer Review in NLP? | Peer review is our best tool for judging the quality of conference
submissions, but it is becoming increasingly spurious. We argue that a part of
the problem is that the reviewers and area chairs face a poorly defined task
forcing apples-to-oranges comparisons. There are several potential ways
forward, but the key difficulty is creating the incentives and mechanisms for
their consistent implementation in the NLP community.
| 2,020 | Computation and Language |
A Co-Interactive Transformer for Joint Slot Filling and Intent Detection | Intent detection and slot filling are two main tasks for building a spoken
language understanding (SLU) system. The two tasks are closely related and the
information of one task can be utilized in the other task. Previous studies
either model the two tasks separately or only consider the single information
flow from intent to slot. None of the prior approaches model the bidirectional
connection between the two tasks simultaneously. In this paper, we propose a
Co-Interactive Transformer to consider the cross-impact between the two tasks.
Instead of adopting the self-attention mechanism in vanilla Transformer, we
propose a co-interactive module to consider the cross-impact by building a
bidirectional connection between the two related tasks. In addition, the
proposed co-interactive module can be stacked to incrementally enhance each
other with mutual features. The experimental results on two public datasets
(SNIPS and ATIS) show that our model achieves the state-of-the-art performance
with considerable improvements (+3.4% and +0.9% on overall acc). Extensive
experiments empirically verify that our model successfully captures the mutual
interaction knowledge.
| 2,021 | Computation and Language |
Large Product Key Memory for Pretrained Language Models | Product key memory (PKM) proposed by Lample et al. (2019) enables to improve
prediction accuracy by increasing model capacity efficiently with insignificant
computational overhead. However, their empirical application is only limited to
causal language modeling. Motivated by the recent success of pretrained
language models (PLMs), we investigate how to incorporate large PKM into PLMs
that can be finetuned for a wide variety of downstream NLP tasks. We define a
new memory usage metric, and careful observation using this metric reveals that
most memory slots remain outdated during the training of PKM-augmented models.
To train better PLMs by tackling this issue, we propose simple but effective
solutions: (1) initialization from the model weights pretrained without memory
and (2) augmenting PKM by addition rather than replacing a feed-forward
network. We verify that both of them are crucial for the pretraining of
PKM-augmented PLMs, enhancing memory utilization and downstream performance.
Code and pretrained weights are available at
https://github.com/clovaai/pkm-transformers.
| 2,020 | Computation and Language |
Population Based Training for Data Augmentation and Regularization in
Speech Recognition | Varying data augmentation policies and regularization over the course of
optimization has led to performance improvements over using fixed values. We
show that population based training is a useful tool to continuously search
those hyperparameters, within a fixed budget. This greatly simplifies the
experimental burden and computational cost of finding such optimal schedules.
We experiment in speech recognition by optimizing SpecAugment this way, as well
as dropout. It compares favorably to a baseline that does not change those
hyperparameters over the course of training, with an 8% relative WER
improvement. We obtain 5.18% word error rate on LibriSpeech's test-other.
| 2,020 | Computation and Language |
Injecting Word Information with Multi-Level Word Adapter for Chinese
Spoken Language Understanding | In this paper, we improve Chinese spoken language understanding (SLU) by
injecting word information. Previous studies on Chinese SLU do not consider the
word information, failing to detect word boundaries that are beneficial for
intent detection and slot filling. To address this issue, we propose a
multi-level word adapter to inject word information for Chinese SLU, which
consists of (1) sentence-level word adapter, which directly fuses the sentence
representations of the word information and character information to perform
intent detection and (2) character-level word adapter, which is applied at each
character for selectively controlling weights on word information as well as
character information. Experimental results on two Chinese SLU datasets show
that our model can capture useful word information and achieve state-of-the-art
performance.
| 2,022 | Computation and Language |
Predicting Typological Features in WALS using Language Embeddings and
Conditional Probabilities: \'UFAL Submission to the SIGTYP 2020 Shared Task | We present our submission to the SIGTYP 2020 Shared Task on the prediction of
typological features. We submit a constrained system, predicting typological
features only based on the WALS database. We investigate two approaches. The
simpler of the two is a system based on estimating correlation of feature
values within languages by computing conditional probabilities and mutual
information. The second approach is to train a neural predictor operating on
precomputed language embeddings based on WALS features. Our submitted system
combines the two approaches based on their self-estimated confidence scores. We
reach the accuracy of 70.7% on the test data and rank first in the shared task.
| 2,020 | Computation and Language |
Generating Instructions at Different Levels of Abstraction | When generating technical instructions, it is often convenient to describe
complex objects in the world at different levels of abstraction. A novice user
might need an object explained piece by piece, while for an expert, talking
about the complex object (e.g. a wall or railing) directly may be more succinct
and efficient. We show how to generate building instructions at different
levels of abstraction in Minecraft. We introduce the use of hierarchical
planning to this end, a method from AI planning which can capture the structure
of complex objects neatly. A crowdsourcing evaluation shows that the choice of
abstraction level matters to users, and that an abstraction strategy which
balances low-level and high-level object descriptions compares favorably to
ones which don't.
| 2,020 | Computation and Language |
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating
Open-Domain Dialogue Systems | Automatically evaluating dialogue coherence is a challenging but high-demand
ability for developing high-quality open-domain dialogue systems. However,
current evaluation metrics consider only surface features or utterance-level
semantics, without explicitly considering the fine-grained topic transition
dynamics of dialogue flows. Here, we first consider that the graph structure
constituted with topics in a dialogue can accurately depict the underlying
communication logic, which is a more natural way to produce persuasive metrics.
Capitalized on the topic-level dialogue graph, we propose a new evaluation
metric GRADE, which stands for Graph-enhanced Representations for Automatic
Dialogue Evaluation. Specifically, GRADE incorporates both coarse-grained
utterance-level contextualized representations and fine-grained topic-level
graph representations to evaluate dialogue coherence. The graph representations
are obtained by reasoning over topic-level dialogue graphs enhanced with the
evidence from a commonsense graph, including k-hop neighboring representations
and hop-attention weights. Experimental results show that our GRADE
significantly outperforms other state-of-the-art metrics on measuring diverse
dialogue models in terms of the Pearson and Spearman correlations with human
judgements. Besides, we release a new large-scale human evaluation benchmark to
facilitate future research on automatic metrics.
| 2,020 | Computation and Language |
Precise Task Formalization Matters in Winograd Schema Evaluations | Performance on the Winograd Schema Challenge (WSC), a respected English
commonsense reasoning benchmark, recently rocketed from chance accuracy to 89%
on the SuperGLUE leaderboard, with relatively little corroborating evidence of
a correspondingly large improvement in reasoning ability. We hypothesize that
much of this improvement comes from recent changes in task formalization---the
combination of input specification, loss function, and reuse of pretrained
parameters---by users of the dataset, rather than improvements in the
pretrained model's reasoning ability. We perform an ablation on two Winograd
Schema datasets that interpolates between the formalizations used before and
after this surge, and find (i) framing the task as multiple choice improves
performance by 2-6 points and (ii) several additional techniques, including the
reuse of a pretrained language modeling head, can mitigate the model's extreme
sensitivity to hyperparameters. We urge future benchmark creators to impose
additional structure to minimize the impact of formalization decisions on
reported results.
| 2,020 | Computation and Language |
BERTering RAMS: What and How Much does BERT Already Know About Event
Arguments? -- A Study on the RAMS Dataset | Using the attention map based probing frame-work from (Clark et al., 2019),
we observe that, on the RAMS dataset (Ebner et al., 2020), BERT's attention
heads have modest but well above-chance ability to spot event arguments sans
any training or domain finetuning, vary-ing from a low of 17.77% for Place to a
high of 51.61% for Artifact. Next, we find that linear combinations of these
heads, estimated with approx 11% of available total event argument detection
supervision, can push performance well-higher for some roles - highest two
being Victim (68.29% Accuracy) and Artifact(58.82% Accuracy). Furthermore, we
investigate how well our methods do for cross-sentence event arguments. We
propose a procedure to isolate "best heads" for cross-sentence argument
detection separately of those for intra-sentence arguments. The heads thus
estimated have superior cross-sentence performance compared to their jointly
estimated equivalents, albeit only under the unrealistic assumption that we
already know the argument is present in an-other sentence. Lastly, we seek to
isolate to what extent our numbers stem from lexical frequency based
associations between gold arguments and roles. We propose NONCE, a scheme to
create adversarial test examples by replacing gold arguments with randomly
generated "nonce" words. We find that learnt linear combinations are robust to
NONCE, though individual best heads can be more sensitive.
| 2,020 | Computation and Language |
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial
Explanations of Their Behavior in Natural Language? | Data collection for natural language (NL) understanding tasks has
increasingly included human explanations alongside data points, allowing past
works to introduce models that both perform a task and generate NL explanations
for their outputs. Yet to date, model-generated explanations have been
evaluated on the basis of surface-level similarities to human explanations,
both through automatic metrics like BLEU and human evaluations. We argue that
these evaluations are insufficient, since they fail to indicate whether
explanations support actual model behavior (faithfulness), rather than simply
match what a human would say (plausibility). In this work, we address the
problem of evaluating explanations from the model simulatability perspective.
Our contributions are as follows: (1) We introduce a leakage-adjusted
simulatability (LAS) metric for evaluating NL explanations, which measures how
well explanations help an observer predict a model's output, while controlling
for how explanations can directly leak the output. We use a model as a proxy
for a human observer, and validate this choice with two human subject
experiments. (2) Using the CoS-E and e-SNLI datasets, we evaluate two existing
generative graphical models and two new approaches; one rationalizing method we
introduce achieves roughly human-level LAS scores. (3) Lastly, we frame
explanation generation as a multi-agent game and optimize explanations for
simulatability while penalizing label leakage, which can improve LAS scores. We
provide code for the experiments in this paper at
https://github.com/peterbhase/LAS-NL-Explanations
| 2,020 | Computation and Language |
Towards Topic-Guided Conversational Recommender System | Conversational recommender systems (CRS) aim to recommend high-quality items
to users through interactive conversations. To develop an effective CRS, the
support of high-quality datasets is essential. Existing CRS datasets mainly
focus on immediate requests from users, while lack proactive guidance to the
recommendation scenario. In this paper, we contribute a new CRS dataset named
\textbf{TG-ReDial} (\textbf{Re}commendation through
\textbf{T}opic-\textbf{G}uided \textbf{Dial}og). Our dataset has two major
features. First, it incorporates topic threads to enforce natural semantic
transitions towards the recommendation scenario. Second, it is created in a
semi-automatic way, hence human annotation is more reasonable and controllable.
Based on TG-ReDial, we present the task of topic-guided conversational
recommendation, and propose an effective approach to this task. Extensive
experiments have demonstrated the effectiveness of our approach on three
sub-tasks, namely topic prediction, item recommendation and response
generation. TG-ReDial is available at https://github.com/RUCAIBox/TG-ReDial.
| 2,020 | Computation and Language |
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool | We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for
the general task of labeling structured data with textual descriptions. The
tool is implemented as an interactive application that reduces human efforts in
annotating large quantities of structured data, e.g. in the format of a table
or tree structure. By using a backend sequence-to-sequence model, our system
iteratively analyzes the annotated labels in order to better sample unlabeled
data. In a simulation experiment performed on annotating large quantities of
structured data, DART has been shown to reduce the total number of annotations
needed with active learning and automatically suggesting relevant labels.
| 2,020 | Computation and Language |
PoinT-5: Pointer Network and T-5 based Financial NarrativeSummarisation | Companies provide annual reports to their shareholders at the end of the
financial year that describes their operations and financial conditions. The
average length of these reports is 80, and it may extend up to 250 pages long.
In this paper, we propose our methodology PoinT-5 (the combination of Pointer
Network and T-5 (Test-to-text transfer Transformer) algorithms) that we used in
the Financial Narrative Summarisation (FNS) 2020 task. The proposed method uses
pointer networks to extract important narrative sentences from the report, and
then T-5 is used to paraphrase extracted sentences into a concise yet
informative sentence. We evaluate our method using ROUGE-N (1,2), L, and SU4.
The proposed method achieves the highest precision scores in all the metrics
and highest F1 scores in ROUGE1, and LCS and the only solution to cross the
MUSE solution baseline in ROUGE-LCS metrics.
| 2,020 | Computation and Language |
Query-Key Normalization for Transformers | Low-resource language translation is a challenging but socially valuable NLP
task. Building on recent work adapting the Transformer's normalization to this
setting, we propose QKNorm, a normalization technique that modifies the
attention mechanism to make the softmax function less prone to arbitrary
saturation without sacrificing expressivity. Specifically, we apply $\ell_2$
normalization along the head dimension of each query and key matrix prior to
multiplying them and then scale up by a learnable parameter instead of dividing
by the square root of the embedding dimension. We show improvements averaging
0.928 BLEU over state-of-the-art bilingual benchmarks for 5 low-resource
translation pairs from the TED Talks corpus and IWSLT'15.
| 2,020 | Computation and Language |
Dual Inference for Improving Language Understanding and Generation | Natural language understanding (NLU) and Natural language generation (NLG)
tasks hold a strong dual relationship, where NLU aims at predicting semantic
labels based on natural language utterances and NLG does the opposite. The
prior work mainly focused on exploiting the duality in model training in order
to obtain the models with better performance. However, regarding the
fast-growing scale of models in the current NLP area, sometimes we may have
difficulty retraining whole NLU and NLG models. To better address the issue,
this paper proposes to leverage the duality in the inference stage without the
need of retraining. The experiments on three benchmark datasets demonstrate the
effectiveness of the proposed method in both NLU and NLG, providing the great
potential of practical usage.
| 2,020 | Computation and Language |
Evaluating the Effectiveness of Efficient Neural Architecture Search for
Sentence-Pair Tasks | Neural Architecture Search (NAS) methods, which automatically learn entire
neural model or individual neural cell architectures, have recently achieved
competitive or state-of-the-art (SOTA) performance on variety of natural
language processing and computer vision tasks, including language modeling,
natural language inference, and image classification. In this work, we explore
the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search
(ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and
semantic textual similarity. We use ENAS to perform a micro-level search and
learn a task-optimized RNN cell architecture as a drop-in replacement for an
LSTM. We explore the effectiveness of ENAS through experiments on three
datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and
two sets of embeddings (Glove, BERT). In contrast to prior work applying ENAS
to NLP tasks, our results are mixed -- we find that ENAS architectures
sometimes, but not always, outperform LSTMs and perform similarly to random
architecture search.
| 2,020 | Computation and Language |
Fake Reviews Detection through Analysis of Linguistic Features | Online reviews play an integral part for success or failure of businesses.
Prior to purchasing services or goods, customers first review the online
comments submitted by previous customers. However, it is possible to
superficially boost or hinder some businesses through posting counterfeit and
fake reviews. This paper explores a natural language processing approach to
identify fake reviews. We present a detailed analysis of linguistic features
for distinguishing fake and trustworthy online reviews. We study 15 linguistic
features and measure their significance and importance towards the
classification schemes employed in this study. Our results indicate that fake
reviews tend to include more redundant terms and pauses, and generally contain
longer sentences. The application of several machine learning classification
algorithms revealed that we were able to discriminate fake from real reviews
with high accuracy using these linguistic features.
| 2,020 | Computation and Language |
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent
Systems | Training an end-to-end (E2E) neural network speech-to-intent (S2I) system
that directly extracts intents from speech requires large amounts of
intent-labeled speech data, which is time consuming and expensive to collect.
Initializing the S2I model with an ASR model trained on copious speech data can
alleviate data sparsity. In this paper, we attempt to leverage NLU text
resources. We implemented a CTC-based S2I system that matches the performance
of a state-of-the-art, traditional cascaded SLU system. We performed controlled
experiments with varying amounts of speech and text training data. When only a
tenth of the original data is available, intent classification accuracy
degrades by 7.6% absolute. Assuming we have additional text-to-intent data
(without speech) available, we investigated two techniques to improve the S2I
system: (1) transfer learning, in which acoustic embeddings for intent
classification are tied to fine-tuned BERT text embeddings; and (2) data
augmentation, in which the text-to-intent data is converted into
speech-to-intent data using a multi-speaker text-to-speech system. The proposed
approaches recover 80% of performance lost due to using limited intent-labeled
speech.
| 2,020 | Computation and Language |
On the Role of Style in Parsing Speech with Neural Models | The differences in written text and conversational speech are substantial;
previous parsers trained on treebanked text have given very poor results on
spontaneous speech. For spoken language, the mismatch in style also extends to
prosodic cues, though it is less well understood. This paper re-examines the
use of written text in parsing speech in the context of recent advances in
neural language processing. We show that neural approaches facilitate using
written text to improve parsing of spontaneous speech, and that prosody further
improves over this state-of-the-art result. Further, we find an asymmetric
degradation from read vs. spontaneous mismatch, with spontaneous speech more
generally useful for training parsers.
| 2,020 | Computation and Language |
comp-syn: Perceptually Grounded Word Embeddings with Color | Popular approaches to natural language processing create word embeddings
based on textual co-occurrence patterns, but often ignore embodied, sensory
aspects of language. Here, we introduce the Python package comp-syn, which
provides grounded word embeddings based on the perceptually uniform color
distributions of Google Image search results. We demonstrate that comp-syn
significantly enriches models of distributional semantics. In particular, we
show that (1) comp-syn predicts human judgments of word concreteness with
greater accuracy and in a more interpretable fashion than word2vec using
low-dimensional word-color embeddings, and (2) comp-syn performs comparably to
word2vec on a metaphorical vs. literal word-pair classification task. comp-syn
is open-source on PyPi and is compatible with mainstream machine-learning
Python packages. Our package release includes word-color embeddings for over
40,000 English words, each associated with crowd-sourced word concreteness
judgments.
| 2,020 | Computation and Language |
Analysis of Disfluency in Children's Speech | Disfluencies are prevalent in spontaneous speech, as shown in many studies of
adult speech. Less is understood about children's speech, especially in
pre-school children who are still developing their language skills. We present
a novel dataset with annotated disfluencies of spontaneous explanations from 26
children (ages 5--8), interviewed twice over a year-long period. Our
preliminary analysis reveals significant differences between children's speech
in our corpus and adult spontaneous speech from two corpora (Switchboard and
CallHome). Children have higher disfluency and filler rates, tend to use nasal
filled pauses more frequently, and on average exhibit longer reparandums than
repairs, in contrast to adult speakers. Despite the differences, an automatic
disfluency detection system trained on adult (Switchboard) speech transcripts
performs reasonably well on children's speech, achieving an F1 score that is
10\% higher than the score on an adult out-of-domain dataset (CallHome).
| 2,020 | Computation and Language |
Learning to Evaluate Translation Beyond English: BLEURT Submissions to
the WMT Metrics 2020 Shared Task | The quality of machine translation systems has dramatically improved over the
last decade, and as a result, evaluation has become an increasingly challenging
problem. This paper describes our contribution to the WMT 2020 Metrics Shared
Task, the main benchmark for automatic evaluation of translation. We make
several submissions based on BLEURT, a previously published metric based on
transfer learning. We extend the metric beyond English and evaluate it on 14
language pairs for which fine-tuning data is available, as well as 4
"zero-shot" language pairs, for which we have no labelled examples.
Additionally, we focus on English to German and demonstrate how to combine
BLEURT's predictions with those of YiSi and use alternative reference
translations to enhance the performance. Empirical results show that the models
achieve competitive results on the WMT Metrics 2019 Shared Task, indicating
their promise for the 2020 edition.
| 2,020 | Computation and Language |
Masked ELMo: An evolution of ELMo towards fully contextual RNN language
models | This paper presents Masked ELMo, a new RNN-based model for language model
pre-training, evolved from the ELMo language model. Contrary to ELMo which only
uses independent left-to-right and right-to-left contexts, Masked ELMo learns
fully bidirectional word representations. To achieve this, we use the same
Masked language model objective as BERT. Additionally, thanks to optimizations
on the LSTM neuron, the integration of mask accumulation and bidirectional
truncated backpropagation through time, we have increased the training speed of
the model substantially. All these improvements make it possible to pre-train a
better language model than ELMo while maintaining a low computational cost. We
evaluate Masked ELMo by comparing it to ELMo within the same protocol on the
GLUE benchmark, where our model outperforms significantly ELMo and is
competitive with transformer approaches.
| 2,020 | Computation and Language |
How Can Self-Attention Networks Recognize Dyck-n Languages? | We focus on the recognition of Dyck-n ($\mathcal{D}_n$) languages with
self-attention (SA) networks, which has been deemed to be a difficult task for
these networks. We compare the performance of two variants of SA, one with a
starting symbol (SA$^+$) and one without (SA$^-$). Our results show that SA$^+$
is able to generalize to longer sequences and deeper dependencies. For
$\mathcal{D}_2$, we find that SA$^-$ completely breaks down on long sequences
whereas the accuracy of SA$^+$ is 58.82$\%$. We find attention maps learned by
$\text{SA}{^+}$ to be amenable to interpretation and compatible with a
stack-based language recognizer. Surprisingly, the performance of SA networks
is at par with LSTMs, which provides evidence on the ability of SA to learn
hierarchies without recursion.
| 2,020 | Computation and Language |
Dynamic Context Selection for Document-level Neural Machine Translation
via Reinforcement Learning | Document-level neural machine translation has yielded attractive
improvements. However, majority of existing methods roughly use all context
sentences in a fixed scope. They neglect the fact that different source
sentences need different sizes of context. To address this problem, we propose
an effective approach to select dynamic context so that the document-level
translation model can utilize the more useful selected context sentences to
produce better translations. Specifically, we introduce a selection module that
is independent of the translation module to score each candidate context
sentence. Then, we propose two strategies to explicitly select a variable
number of context sentences and feed them into the translation module. We train
the two modules end-to-end via reinforcement learning. A novel reward is
proposed to encourage the selection and utilization of dynamic context
sentences. Experiments demonstrate that our approach can select adaptive
context sentences for different source sentences, and significantly improves
the performance of document-level translation methods.
| 2,020 | Computation and Language |
Langsmith: An Interactive Academic Text Revision System | Despite the current diversity and inclusion initiatives in the academic
community, researchers with a non-native command of English still face
significant obstacles when writing papers in English. This paper presents the
Langsmith editor, which assists inexperienced, non-native researchers to write
English papers, especially in the natural language processing (NLP) field. Our
system can suggest fluent, academic-style sentences to writers based on their
rough, incomplete phrases or sentences. The system also encourages interaction
between human writers and the computerized revision system. The experimental
results demonstrated that Langsmith helps non-native English-speaker students
write papers in English. The system is available at https://emnlp-demo.editor.
langsmith.co.jp/.
| 2,020 | Computation and Language |
NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative
COVID-19 Tweets using Ensembling and Adversarial Training | We experiment with COVID-Twitter-BERT and RoBERTa models to identify
informative COVID-19 tweets. We further experiment with adversarial training to
make our models robust. The ensemble of COVID-Twitter-BERT and RoBERTa obtains
a F1-score of 0.9096 (on the positive class) on the test data of WNUT-2020 Task
2 and ranks 1st on the leaderboard. The ensemble of the models trained using
adversarial training also produces similar result.
| 2,020 | Computation and Language |
Plug-and-Play Conversational Models | There has been considerable progress made towards conversational models that
generate coherent and fluent responses; however, this often involves training
large language models on large dialogue datasets, such as Reddit. These large
conversational models provide little control over the generated responses, and
this control is further limited in the absence of annotated conversational
datasets for attribute specific generation that can be used for fine-tuning the
model. In this paper, we first propose and evaluate plug-and-play methods for
controllable response generation, which does not require dialogue specific
datasets and does not rely on fine-tuning a large model. While effective, the
decoding procedure induces considerable computational overhead, rendering the
conversational model unsuitable for interactive usage. To overcome this, we
introduce an approach that does not require further computation at decoding
time, while also does not require any fine-tuning of a large language model. We
demonstrate, through extensive automatic and human evaluation, a high degree of
control over the generated conversational responses with regard to multiple
desired attributes, while being fluent.
| 2,020 | Computation and Language |
Style Attuned Pre-training and Parameter Efficient Fine-tuning for
Spoken Language Understanding | Neural models have yielded state-of-the-art results in deciphering spoken
language understanding (SLU) problems; however, these models require a
significant amount of domain-specific labeled examples for training, which is
prohibitively expensive. While pre-trained language models like BERT have been
shown to capture a massive amount of knowledge by learning from unlabeled
corpora and solve SLU using fewer labeled examples for adaption, the encoding
of knowledge is implicit and agnostic to downstream tasks. Such encoding
results in model inefficiencies in parameter usage: an entirely new model is
required for every domain. To address these challenges, we introduce a novel
SLU framework, comprising a conversational language modeling (CLM) pre-training
task and a light encoder architecture. The CLM pre-training enables networks to
capture the representation of the language in conversation style with the
presence of ASR errors. The light encoder architecture separates the shared
pre-trained networks from the mappings of generally encoded knowledge to
specific domains of SLU, allowing for the domain adaptation to be performed
solely at the light encoder and thus increasing efficiency. With the framework,
we match the performance of state-of-the-art SLU results on Alexa internal
datasets and on two public ones (ATIS, SNIPS), adding only 4.4% parameters per
task.
| 2,020 | Computation and Language |
Constrained Decoding for Computationally Efficient Named Entity
Recognition Taggers | Current state-of-the-art models for named entity recognition (NER) are neural
models with a conditional random field (CRF) as the final layer. Entities are
represented as per-token labels with a special structure in order to decode
them into spans. Current work eschews prior knowledge of how the span encoding
scheme works and relies on the CRF learning which transitions are illegal and
which are not to facilitate global coherence. We find that by constraining the
output to suppress illegal transitions we can train a tagger with a
cross-entropy loss twice as fast as a CRF with differences in F1 that are
statistically insignificant, effectively eliminating the need for a CRF. We
analyze the dynamics of tag co-occurrence to explain when these constraints are
most effective and provide open source implementations of our tagger in both
PyTorch and TensorFlow.
| 2,020 | Computation and Language |
Pragmatically Informative Color Generation by Grounding Contextual
Modifiers | Grounding language in contextual information is crucial for fine-grained
natural language understanding. One important task that involves grounding
contextual modifiers is color generation. Given a reference color "green", and
a modifier "bluey", how does one generate a color that could represent "bluey
green"? We propose a computational pragmatics model that formulates this color
generation task as a recursive game between speakers and listeners. In our
model, a pragmatic speaker reasons about the inferences that a listener would
make, and thus generates a modified color that is maximally informative to help
the listener recover the original referents. In this paper, we show that
incorporating pragmatic information provides significant improvements in
performance compared with other state-of-the-art deep learning models where
pragmatic inference and flexibility in representing colors from a large
continuous space are lacking. Our model has an absolute 98% increase in
performance for the test cases where the reference colors are unseen during
training, and an absolute 40% increase in performance for the test cases where
both the reference colors and the modifiers are unseen during training.
| 2,020 | Computation and Language |
iobes: A Library for Span-Level Processing | Many tasks in natural language processing, such as named entity recognition
and slot-filling, involve identifying and labeling specific spans of text. In
order to leverage common models, these tasks are often recast as sequence
labeling tasks. Each token is given a label and these labels are prefixed with
special tokens such as B- or I-. After a model assigns labels to each token,
these prefixes are used to group the tokens into spans.
Properly parsing these annotations is critical for producing fair and
comparable metrics; however, despite its importance, there is not an
easy-to-use, standardized, programmatically integratable library to help work
with span labeling. To remedy this, we introduce our open-source library,
iobes. iobes is used for parsing, converting, and processing spans represented
as token level decisions.
| 2,020 | Computation and Language |
Q-learning with Language Model for Edit-based Unsupervised Summarization | Unsupervised methods are promising for abstractive text summarization in that
the parallel corpora is not required. However, their performance is still far
from being satisfied, therefore research on promising solutions is on-going. In
this paper, we propose a new approach based on Q-learning with an edit-based
summarization. The method combines two key modules to form an Editorial Agent
and Language Model converter (EALM). The agent predicts edit actions (e.t.,
delete, keep, and replace), and then the LM converter deterministically
generates a summary on the basis of the action signals. Q-learning is leveraged
to train the agent to produce proper edit actions. Experimental results show
that EALM delivered competitive performance compared with the previous
encoder-decoder-based methods, even with truly zero paired data (i.e., no
validation set). Defining the task as Q-learning enables us not only to develop
a competitive method but also to make the latest techniques in reinforcement
learning available for unsupervised summarization. We also conduct qualitative
analysis, providing insights into future study on unsupervised summarizers.
| 2,020 | Computation and Language |
Token-level Adaptive Training for Neural Machine Translation | There exists a token imbalance phenomenon in natural language as different
tokens appear with different frequencies, which leads to different learning
difficulties for tokens in Neural Machine Translation (NMT). The vanilla NMT
model usually adopts trivial equal-weighted objectives for target tokens with
different frequencies and tends to generate more high-frequency tokens and less
low-frequency tokens compared with the golden token distribution. However,
low-frequency tokens may carry critical semantic information that will affect
the translation quality once they are neglected. In this paper, we explored
target token-level adaptive objectives based on token frequencies to assign
appropriate weights for each target token during training. We aimed that those
meaningful but relatively low-frequency words could be assigned with larger
weights in objectives to encourage the model to pay more attention to these
tokens. Our method yields consistent improvements in translation quality on
ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain
more low-frequency tokens where we can get 1.68, 1.02, and 0.52 BLEU increases
compared with baseline, respectively. Further analyses show that our method can
also improve the lexical diversity of translation.
| 2,020 | Computation and Language |
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text
Generation | AMR-to-text generation is used to transduce Abstract Meaning Representation
structures (AMR) into text. A key challenge in this task is to efficiently
learn effective graph representations. Previously, Graph Convolution Networks
(GCNs) were used to encode input AMRs, however, vanilla GCNs are not able to
capture non-local information and additionally, they follow a local
(first-order) information aggregation scheme. To account for these issues,
larger and deeper GCN models are required to capture more complex interactions.
In this paper, we introduce a dynamic fusion mechanism, proposing Lightweight
Dynamic Graph Convolutional Networks (LDGCNs) that capture richer non-local
interactions by synthesizing higher order information from the input graphs. We
further develop two novel parameter saving strategies based on the group graph
convolutions and weight tied convolutions to reduce memory usage and model
complexity. With the help of these strategies, we are able to train a model
with fewer parameters while maintaining the model capacity. Experiments
demonstrate that LDGCNs outperform state-of-the-art models on two benchmark
datasets for AMR-to-text generation with significantly fewer parameters.
| 2,020 | Computation and Language |
Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of
Natural Language Processing Contributions -- A Trial Dataset | Purpose: The aim of this work is to normalize the NLPCONTRIBUTIONS scheme
(henceforward, NLPCONTRIBUTIONGRAPH) to structure, directly from article
sentences, the contributions information in Natural Language Processing (NLP)
scholarly articles via a two-stage annotation methodology: 1) pilot stage - to
define the scheme (described in prior work); and 2) adjudication stage - to
normalize the graphing model (the focus of this paper).
Design/methodology/approach: We re-annotate, a second time, the
contributions-pertinent information across 50 prior-annotated NLP scholarly
articles in terms of a data pipeline comprising: contribution-centered
sentences, phrases, and triple statements. To this end, specifically, care was
taken in the adjudication annotation stage to reduce annotation noise while
formulating the guidelines for our proposed novel NLP contributions structuring
and graphing scheme.
Findings: The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted
finally in a dataset of 900 contribution-focused sentences, 4,702
contribution-information-centered phrases, and 2,980 surface-structured
triples. The intra-annotation agreement between the first and second stages, in
terms of F1, was 67.92% for sentences, 41.82% for phrases, and 22.31% for
triple statements indicating that with increased granularity of the
information, the annotation decision variance is greater.
Practical Implications: We demonstrate NLPCONTRIBUTIONGRAPH data integrated
into the Open Research Knowledge Graph (ORKG), a next-generation KG-based
digital library with intelligent computations enabled over structured scholarly
knowledge, as a viable aid to assist researchers in their day-to-day tasks.
| 2,021 | Computation and Language |
A Survey of Knowledge-Enhanced Text Generation | The goal of text generation is to make machines express in human language. It
is one of the most important yet challenging tasks in natural language
processing (NLP). Since 2014, various neural encoder-decoder models pioneered
by Seq2Seq have been proposed to achieve the goal by learning to map input text
to output text. However, the input text alone often provides limited knowledge
to generate the desired output, so the performance of text generation is still
far from satisfaction in many real-world scenarios. To address this issue,
researchers have considered incorporating various forms of knowledge beyond the
input text into the generation models. This research direction is known as
knowledge-enhanced text generation. In this survey, we present a comprehensive
review of the research on knowledge enhanced text generation over the past five
years. The main content includes two parts: (i) general methods and
architectures for integrating knowledge into text generation; (ii) specific
techniques and applications according to different forms of knowledge data.
This survey can have broad audiences, researchers and practitioners, in
academia and industry.
| 2,022 | Computation and Language |
gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM
Architecture for SENTIment Analysis of Code-MIXed Data | The phenomenon of mixing the vocabulary and syntax of multiple languages
within the same utterance is called Code-Mixing. This is more evident in
multilingual societies. In this paper, we have developed a system for SemEval
2020: Task 9 on Sentiment Analysis for Code-Mixed Social Media Text. Our system
first generates two types of embeddings for the social media text. In those,
the first one is character level embeddings to encode the character level
information and to handle the out-of-vocabulary entries and the second one is
FastText word embeddings for capturing morphology and semantics. These two
embeddings were passed to the LSTM network and the system outperformed the
baseline model.
| 2,020 | Computation and Language |
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation | As a sequence-to-sequence generation task, neural machine translation (NMT)
naturally contains intrinsic uncertainty, where a single sentence in one
language has multiple valid counterparts in the other. However, the dominant
methods for NMT only observe one of them from the parallel corpora for the
model training but have to deal with adequate variations under the same meaning
at inference. This leads to a discrepancy of the data distribution between the
training and the inference phases. To address this problem, we propose
uncertainty-aware semantic augmentation, which explicitly captures the
universal semantic information among multiple semantically-equivalent source
sentences and enhances the hidden representations with this information for
better translations. Extensive experiments on various translation tasks reveal
that our approach significantly outperforms the strong baselines and the
existing methods.
| 2,020 | Computation and Language |
Multichannel Generative Language Model: Learning All Possible
Factorizations Within and Across Channels | A channel corresponds to a viewpoint or transformation of an underlying
meaning. A pair of parallel sentences in English and French express the same
underlying meaning, but through two separate channels corresponding to their
languages. In this work, we present the Multichannel Generative Language Model
(MGLM). MGLM is a generative joint distribution model over channels. MGLM
marginalizes over all possible factorizations within and across all channels.
MGLM endows flexible inference, including unconditional generation, conditional
generation (where 1 channel is observed and other channels are generated), and
partially observed generation (where incomplete observations are spread across
all the channels). We experiment with the Multi30K dataset containing English,
French, Czech, and German. We demonstrate experiments with unconditional,
conditional, and partially conditional generation. We provide qualitative
samples sampled unconditionally from the generative joint distribution. We also
quantitatively analyze the quality-diversity trade-offs and find MGLM
outperforms traditional bilingual discriminative models.
| 2,020 | Computation and Language |
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset | We present MLQE-PE, a new dataset for Machine Translation (MT) Quality
Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven
language pairs, with human labels for up to 10,000 translations per language
pair in the following formats: sentence-level direct assessments and
post-editing effort, and word-level good/bad labels. It also contains the
post-edited sentences, as well as titles of the articles where the sentences
were extracted from, and the neural MT models used to translate the text.
| 2,021 | Computation and Language |
Word Level Language Identification in English Telugu Code Mixed Data | In a multilingual or sociolingual configuration Intra-sentential Code
Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the
world, most of the people know more than one language. CM usage is especially
apparent in social media platforms. Moreover, ICS is particularly significant
in the context of technology, health, and law where conveying the upcoming
developments are difficult in one's native language. In applications like
dialog systems, machine translation, semantic parsing, shallow parsing, etc. CM
and Code Switching pose serious challenges. To do any further advancement in
code-mixed data, the necessary step is Language Identification. In this paper,
we present a study of various models - Nave Bayes Classifier, Random Forest
Classifier, Conditional Random Field (CRF), and Hidden Markov Model (HMM) for
Language Identification in English - Telugu Code Mixed Data. Considering the
paucity of resources in code mixed languages, we proposed the CRF model and HMM
model for word level language identification. Our best performing system is
CRF-based with an f1-score of 0.91.
| 2,020 | Computation and Language |
Top-Rank-Focused Adaptive Vote Collection for the Evaluation of
Domain-Specific Semantic Models | The growth of domain-specific applications of semantic models, boosted by the
recent achievements of unsupervised embedding learning algorithms, demands
domain-specific evaluation datasets. In many cases, content-based recommenders
being a prime example, these models are required to rank words or texts
according to their semantic relatedness to a given concept, with particular
focus on top ranks. In this work, we give a threefold contribution to address
these requirements: (i) we define a protocol for the construction, based on
adaptive pairwise comparisons, of a relatedness-based evaluation dataset
tailored on the available resources and optimized to be particularly accurate
in top-rank evaluation; (ii) we define appropriate metrics, extensions of
well-known ranking correlation coefficients, to evaluate a semantic model via
the aforementioned dataset by taking into account the greater significance of
top ranks. Finally, (iii) we define a stochastic transitivity model to simulate
semantic-driven pairwise comparisons, which confirms the effectiveness of the
proposed dataset construction protocol.
| 2,020 | Computation and Language |
Self-Paced Learning for Neural Machine Translation | Recent studies have proven that the training of neural machine translation
(NMT) can be facilitated by mimicking the learning process of humans.
Nevertheless, achievements of such kind of curriculum learning rely on the
quality of artificial schedule drawn up with the handcrafted features, e.g.
sentence length or word rarity. We ameliorate this procedure with a more
flexible manner by proposing self-paced learning, where NMT model is allowed to
1) automatically quantify the learning confidence over training examples; and
2) flexibly govern its learning via regulating the loss in each iteration step.
Experimental results over multiple translation tasks demonstrate that the
proposed model yields better performance than strong baselines and those models
trained with human-designed curricula on both translation quality and
convergence speed.
| 2,022 | Computation and Language |
Online Back-Parsing for AMR-to-Text Generation | AMR-to-text generation aims to recover a text containing the same meaning as
an input AMR graph. Current research develops increasingly powerful graph
encoders to better represent AMR graphs, with decoders based on standard
language modeling being used to generate outputs. We propose a decoder that
back predicts projected AMR graphs on the target sentence during text
generation. As the result, our outputs can better preserve the input meaning
than standard decoders. Experiments on two AMR benchmarks show the superiority
of our model over the previous state-of-the-art system based on graph
Transformer.
| 2,020 | Computation and Language |
What Have We Achieved on Text Summarization? | Deep learning has led to significant improvement in text summarization with
various methods investigated and improved ROUGE scores reported over the years.
However, gaps still exist between summaries produced by automatic summarizers
and human professionals. Aiming to gain more understanding of summarization
systems with respect to their strengths and limits on a fine-grained syntactic
and semantic level, we consult the Multidimensional Quality Metric(MQM) and
quantify 8 major sources of errors on 10 representative summarization models
manually. Primarily, we find that 1) under similar settings, extractive
summarizers are in general better than their abstractive counterparts thanks to
strength in faithfulness and factual-consistency; 2) milestone techniques such
as copy, coverage and hybrid extractive/abstractive methods do bring specific
improvements but also demonstrate limitations; 3) pre-training techniques, and
in particular sequence-to-sequence pre-training, are highly effective for
improving text summarization, with BART giving the best results.
| 2,020 | Computation and Language |
Measuring What Counts: The case of Rumour Stance Classification | Stance classification can be a powerful tool for understanding whether and
which users believe in online rumours. The task aims to automatically predict
the stance of replies towards a given rumour, namely support, deny, question,
or comment. Numerous methods have been proposed and their performance compared
in the RumourEval shared tasks in 2017 and 2019. Results demonstrated that this
is a challenging problem since naturally occurring rumour stance data is highly
imbalanced. This paper specifically questions the evaluation metrics used in
these shared tasks. We re-evaluate the systems submitted to the two RumourEval
tasks and show that the two widely adopted metrics -- accuracy and macro-F1 --
are not robust for the four-class imbalanced task of rumour stance
classification, as they wrongly favour systems with highly skewed accuracy
towards the majority class. To overcome this problem, we propose new evaluation
metrics for rumour stance detection. These are not only robust to imbalanced
data but also score higher systems that are capable of recognising the two most
informative minority classes (support and deny).
| 2,020 | Computation and Language |
Toxic Language Detection in Social Media for Brazilian Portuguese: New
Dataset and Multilingual Analysis | Hate speech and toxic comments are a common concern of social media platform
users. Although these comments are, fortunately, the minority in these
platforms, they are still capable of causing harm. Therefore, identifying these
comments is an important task for studying and preventing the proliferation of
toxicity in social media. Previous work in automatically detecting toxic
comments focus mainly in English, with very few work in languages like
Brazilian Portuguese. In this paper, we propose a new large-scale dataset for
Brazilian Portuguese with tweets annotated as either toxic or non-toxic or in
different types of toxicity. We present our dataset collection and annotation
process, where we aimed to select candidates covering multiple demographic
groups. State-of-the-art BERT models were able to achieve 76% macro-F1 score
using monolingual data in the binary case. We also show that large-scale
monolingual data is still needed to create more accurate models, despite recent
advances in multilingual approaches. An error analysis and experiments with
multi-label classification show the difficulty of classifying certain types of
toxic comments that appear less frequently in our data and highlights the need
to develop models that are aware of different categories of toxicity.
| 2,020 | Computation and Language |
HENIN: Learning Heterogeneous Neural Interaction Networks for
Explainable Cyberbullying Detection on Social Media | In the computational detection of cyberbullying, existing work largely
focused on building generic classifiers that rely exclusively on text analysis
of social media sessions. Despite their empirical success, we argue that a
critical missing piece is the model explainability, i.e., why a particular
piece of media session is detected as cyberbullying. In this paper, therefore,
we propose a novel deep model, HEterogeneous Neural Interaction Networks
(HENIN), for explainable cyberbullying detection. HENIN contains the following
components: a comment encoder, a post-comment co-attention sub-network, and
session-session and post-post interaction extractors. Extensive experiments
conducted on real datasets exhibit not only the promising performance of HENIN,
but also highlight evidential comments so that one can understand why a media
session is identified as cyberbullying.
| 2,020 | Computation and Language |
Denoising Multi-Source Weak Supervision for Neural Text Classification | We study the problem of learning neural text classifiers without using any
labeled data, but only easy-to-provide rules as multiple weak supervision
sources. This problem is challenging because rule-induced weak labels are often
noisy and incomplete. To address these two challenges, we design a label
denoiser, which estimates the source reliability using a conditional soft
attention mechanism and then reduces label noise by aggregating rule-annotated
weak labels. The denoised pseudo labels then supervise a neural classifier to
predicts soft labels for unmatched samples, which address the rule coverage
issue. We evaluate our model on five benchmarks for sentiment, topic, and
relation classifications. The results show that our model outperforms
state-of-the-art weakly-supervised and semi-supervised methods consistently,
and achieves comparable performance with fully-supervised methods even without
any labeled data. Our code can be found at
https://github.com/weakrules/Denoise-multi-weak-sources.
| 2,021 | Computation and Language |
Mark-Evaluate: Assessing Language Generation using Population Estimation
Methods | We propose a family of metrics to assess language generation derived from
population estimation methods widely used in ecology. More specifically, we use
mark-recapture and maximum-likelihood methods that have been applied over the
past several decades to estimate the size of closed populations in the wild. We
propose three novel metrics: ME$_\text{Petersen}$ and ME$_\text{CAPTURE}$,
which retrieve a single-valued assessment, and ME$_\text{Schnabel}$ which
returns a double-valued metric to assess the evaluation set in terms of quality
and diversity, separately. In synthetic experiments, our family of methods is
sensitive to drops in quality and diversity. Moreover, our methods show a
higher correlation to human evaluation than existing metrics on several
challenging tasks, namely unconditional language generation, machine
translation, and text summarization.
| 2,020 | Computation and Language |
Examining the Ordering of Rhetorical Strategies in Persuasive Requests | Interpreting how persuasive language influences audiences has implications
across many domains like advertising, argumentation, and propaganda. Persuasion
relies on more than a message's content. Arranging the order of the message
itself (i.e., ordering specific rhetorical strategies) also plays an important
role. To examine how strategy orderings contribute to persuasiveness, we first
utilize a Variational Autoencoder model to disentangle content and rhetorical
strategies in textual requests from a large-scale loan request corpus. We then
visualize interplay between content and strategy through an attentional LSTM
that predicts the success of textual requests. We find that specific (orderings
of) strategies interact uniquely with a request's content to impact success
rate, and thus the persuasiveness of a request.
| 2,020 | Computation and Language |
Recurrent babbling: evaluating the acquisition of grammar from limited
input data | Recurrent Neural Networks (RNNs) have been shown to capture various aspects
of syntax from raw linguistic input. In most previous experiments, however,
learning happens over unrealistic corpora, which do not reflect the type and
amount of data a child would be exposed to. This paper remedies this state of
affairs by training a Long Short-Term Memory network (LSTM) over a
realistically sized subset of child-directed input. The behaviour of the
network is analysed over time using a novel methodology which consists in
quantifying the level of grammatical abstraction in the model's generated
output (its "babbling"), compared to the language it has been exposed to. We
show that the LSTM indeed abstracts new structuresas learning proceeds.
| 2,020 | Computation and Language |
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction | Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting
aspect terms and opinion terms from review in the form of opinion pairs or
additionally extracting sentiment polarity of aspect term to form opinion
triplet. Because of containing several opinion factors, the complete AFOE task
is usually divided into multiple subtasks and achieved in the pipeline.
However, pipeline approaches easily suffer from error propagation and
inconvenience in real-world scenarios. To this end, we propose a novel tagging
scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end
fashion only with one unified grid tagging task. Additionally, we design an
effective inference strategy on GTS to exploit mutual indication between
different opinion factors for more accurate extractions. To validate the
feasibility and compatibility of GTS, we implement three different GTS models
respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the
aspect-oriented opinion pair extraction and opinion triplet extraction
datasets. Extensive experimental results indicate that GTS models outperform
strong baselines significantly and achieve state-of-the-art performance.
| 2,020 | Computation and Language |
High-order Semantic Role Labeling | Semantic role labeling is primarily used to identify predicates, arguments,
and their semantic relationships. Due to the limitations of modeling methods
and the conditions of pre-identified predicates, previous work has focused on
the relationships between predicates and arguments and the correlations between
arguments at most, while the correlations between predicates have been
neglected for a long time. High-order features and structure learning were very
common in modeling such correlations before the neural network era. In this
paper, we introduce a high-order graph structure for the neural semantic role
labeling model, which enables the model to explicitly consider not only the
isolated predicate-argument pairs but also the interaction between the
predicate-argument pairs. Experimental results on 7 languages of the CoNLL-2009
benchmark show that the high-order structural learning techniques are
beneficial to the strong performing SRL models and further boost our baseline
to achieve new state-of-the-art results.
| 2,020 | Computation and Language |
LSTMs Compose (and Learn) Bottom-Up | Recent work in NLP shows that LSTM language models capture hierarchical
structure in language data. In contrast to existing work, we consider the
\textit{learning} process that leads to their compositional behavior. For a
closer look at how an LSTM's sequential representations are composed
hierarchically, we present a related measure of Decompositional Interdependence
(DI) between word meanings in an LSTM, based on their gate interactions. We
connect this measure to syntax with experiments on English language data, where
DI is higher on pairs of words with lower syntactic distance. To explore the
inductive biases that cause these compositional representations to arise during
training, we conduct simple experiments on synthetic data. These synthetic
experiments support a specific hypothesis about how hierarchical structures are
discovered over the course of training: that LSTM constituent representations
are learned bottom-up, relying on effective representations of their shorter
children, rather than learning the longer-range relations independently from
children.
| 2,020 | Computation and Language |
Case Study: Deontological Ethics in NLP | Recent work in natural language processing (NLP) has focused on ethical
challenges such as understanding and mitigating bias in data and algorithms;
identifying objectionable content like hate speech, stereotypes and offensive
language; and building frameworks for better system design and data handling
practices. However, there has been little discussion about the ethical
foundations that underlie these efforts. In this work, we study one ethical
theory, namely deontological ethics, from the perspective of NLP. In
particular, we focus on the generalization principle and the respect for
autonomy through informed consent. We provide four case studies to demonstrate
how these principles can be used with NLP systems. We also recommend directions
to avoid the ethical issues in these systems.
| 2,021 | Computation and Language |
Scaling Systematic Literature Reviews with Machine Learning Pipelines | Systematic reviews, which entail the extraction of data from large numbers of
scientific documents, are an ideal avenue for the application of machine
learning. They are vital to many fields of science and philanthropy, but are
very time-consuming and require experts. Yet the three main stages of a
systematic review are easily done automatically: searching for documents can be
done via APIs and scrapers, selection of relevant documents can be done via
binary classification, and extraction of data can be done via
sequence-labelling classification. Despite the promise of automation for this
field, little research exists that examines the various ways to automate each
of these tasks. We construct a pipeline that automates each of these aspects,
and experiment with many human-time vs. system quality trade-offs. We test the
ability of classifiers to work well on small amounts of data and to generalise
to data from countries not represented in the training data. We test different
types of data extraction with varying difficulty in annotation, and five
different neural architectures to do the extraction. We find that we can get
surprising accuracy and generalisability of the whole pipeline system with only
2 weeks of human-expert annotation, which is only 15% of the time it takes to
do the whole review manually and can be repeated and extended to new data with
no additional effort.
| 2,020 | Computation and Language |
Learning Context-Free Languages with Nondeterministic Stack RNNs | We present a differentiable stack data structure that simultaneously and
tractably encodes an exponential number of stack configurations, based on
Lang's algorithm for simulating nondeterministic pushdown automata. We call the
combination of this data structure with a recurrent neural network (RNN)
controller a Nondeterministic Stack RNN. We compare our model against existing
stack RNNs on various formal languages, demonstrating that our model converges
more reliably to algorithmic behavior on deterministic tasks, and achieves
lower cross-entropy on inherently nondeterministic tasks.
| 2,022 | Computation and Language |
Recursive Top-Down Production for Sentence Generation with Latent Trees | We model the recursive production property of context-free grammars for
natural and synthetic languages. To this end, we present a dynamic programming
algorithm that marginalises over latent binary tree structures with $N$ leaves,
allowing us to compute the likelihood of a sequence of $N$ tokens under a
latent tree model, which we maximise to train a recursive neural function. We
demonstrate performance on two synthetic tasks: SCAN (Lake and Baroni, 2017),
where it outperforms previous models on the LENGTH split, and English question
formation (McCoy et al., 2020), where it performs comparably to decoders with
the ground-truth tree structure. We also present experimental results on
German-English translation on the Multi30k dataset (Elliott et al., 2016), and
qualitatively analyse the induced tree structures our model learns for the SCAN
tasks and the German-English translation task.
| 2,020 | Computation and Language |
Uncertainty over Uncertainty: Investigating the Assumptions,
Annotations, and Text Measurements of Economic Policy Uncertainty | Methods and applications are inextricably linked in science, and in
particular in the domain of text-as-data. In this paper, we examine one such
text-as-data application, an established economic index that measures economic
policy uncertainty from keyword occurrences in news. This index, which is shown
to correlate with firm investment, employment, and excess market returns, has
had substantive impact in both the private sector and academia. Yet, as we
revisit and extend the original authors' annotations and text measurements we
find interesting text-as-data methodological research questions: (1) Are
annotator disagreements a reflection of ambiguity in language? (2) Do
alternative text measurements correlate with one another and with measures of
external predictive validity? We find for this application (1) some annotator
disagreements of economic policy uncertainty can be attributed to ambiguity in
language, and (2) switching measurements from keyword-matching to supervised
machine learning classifiers results in low correlation, a concerning
implication for the validity of the index.
| 2,020 | Computation and Language |
Evaluating and Characterizing Human Rationales | Two main approaches for evaluating the quality of machine-generated
rationales are: 1) using human rationales as a gold standard; and 2) automated
metrics based on how rationales affect model behavior. An open question,
however, is how human rationales fare with these automatic metrics. Analyzing a
variety of datasets and models, we find that human rationales do not
necessarily perform well on these metrics. To unpack this finding, we propose
improved metrics to account for model-dependent baseline performance. We then
propose two methods to further characterize rationale quality, one based on
model retraining and one on using "fidelity curves" to reveal properties such
as irrelevance and redundancy. Our work leads to actionable suggestions for
evaluating and characterizing rationales.
| 2,020 | Computation and Language |
Learning to Pronounce Chinese Without a Pronunciation Dictionary | We demonstrate a program that learns to pronounce Chinese text in Mandarin,
without a pronunciation dictionary. From non-parallel streams of Chinese
characters and Chinese pinyin syllables, it establishes a many-to-many mapping
between characters and pronunciations. Using unsupervised methods, the program
effectively deciphers writing into speech. Its token-level
character-to-syllable accuracy is 89%, which significantly exceeds the 22%
accuracy of prior work.
| 2,020 | Computation and Language |
Solving Historical Dictionary Codes with a Neural Language Model | We solve difficult word-based substitution codes by constructing a decoding
lattice and searching that lattice with a neural language model. We apply our
method to a set of enciphered letters exchanged between US Army General James
Wilkinson and agents of the Spanish Crown in the late 1700s and early 1800s,
obtained from the US Library of Congress. We are able to decipher 75.1% of the
cipher-word tokens correctly.
| 2,020 | Computation and Language |
MEEP: An Open-Source Platform for Human-Human Dialog Collection and
End-to-End Agent Training | We create a new task-oriented dialog platform (MEEP) where agents are given
considerable freedom in terms of utterances and API calls, but are constrained
to work within a push-button environment. We include facilities for collecting
human-human dialog corpora, and for training automatic agents in an end-to-end
fashion. We demonstrate MEEP with a dialog assistant that lets users specify
trip destinations.
| 2,020 | Computation and Language |
Investigating Cross-Linguistic Adjective Ordering Tendencies with a
Latent-Variable Model | Across languages, multiple consecutive adjectives modifying a noun (e.g. "the
big red dog") follow certain unmarked ordering rules. While explanatory
accounts have been put forward, much of the work done in this area has relied
primarily on the intuitive judgment of native speakers, rather than on corpus
data. We present the first purely corpus-driven model of multi-lingual
adjective ordering in the form of a latent-variable model that can accurately
order adjectives across 24 different languages, even when the training and
testing languages are different. We utilize this novel statistical model to
provide strong converging evidence for the existence of universal,
cross-linguistic, hierarchical adjective ordering tendencies.
| 2,020 | Computation and Language |
Counterfactually-Augmented SNLI Training Data Does Not Yield Better
Generalization Than Unaugmented Data | A growing body of work shows that models exploit annotation artifacts to
achieve state-of-the-art performance on standard crowdsourced
benchmarks---datasets collected from crowdworkers to create an evaluation
task---while still failing on out-of-domain examples for the same task. Recent
work has explored the use of counterfactually-augmented data---data built by
minimally editing a set of seed examples to yield counterfactual labels---to
augment training data associated with these benchmarks and build more robust
classifiers that generalize better. However, Khashabi et al. (2020) find that
this type of augmentation yields little benefit on reading comprehension tasks
when controlling for dataset size and cost of collection. We build upon this
work by using English natural language inference data to test model
generalization and robustness and find that models trained on a
counterfactually-augmented SNLI dataset do not generalize better than
unaugmented datasets of similar size and that counterfactual augmentation can
hurt performance, yielding models that are less robust to challenge examples.
Counterfactual augmentation of natural language understanding data through
standard crowdsourcing techniques does not appear to be an effective way of
collecting training data and further innovation is required to make this
general line of work viable.
| 2,020 | Computation and Language |
ChrEn: Cherokee-English Machine Translation for Endangered Language
Revitalization | Cherokee is a highly endangered Native American language spoken by the
Cherokee people. The Cherokee culture is deeply embedded in its language.
However, there are approximately only 2,000 fluent first language Cherokee
speakers remaining in the world, and the number is declining every year. To
help save this endangered language, we introduce ChrEn, a Cherokee-English
parallel dataset, to facilitate machine translation research between Cherokee
and English. Compared to some popular machine translation language pairs, ChrEn
is extremely low-resource, only containing 14k sentence pairs in total. We
split our parallel data in ways that facilitate both in-domain and
out-of-domain evaluation. We also collect 5k Cherokee monolingual data to
enable semi-supervised learning. Besides these datasets, we propose several
Cherokee-English and English-Cherokee machine translation systems. We compare
SMT (phrase-based) versus NMT (RNN-based and Transformer-based) systems;
supervised versus semi-supervised (via language model, back-translation, and
BERT/Multilingual-BERT) methods; as well as transfer learning versus
multilingual joint training with 4 other languages. Our best results are
15.8/12.7 BLEU for in-domain and 6.5/5.0 BLEU for out-of-domain Chr-En/EnChr
translations, respectively, and we hope that our dataset and systems will
encourage future work by the community for Cherokee language revitalization.
Our data, code, and demo will be publicly available at
https://github.com/ZhangShiyue/ChrEn
| 2,020 | Computation and Language |
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic
Training Data | We propose AutoQA, a methodology and toolkit to generate semantic parsers
that answer questions on databases, with no manual effort. Given a database
schema and its data, AutoQA automatically generates a large set of high-quality
questions for training that covers different database operations. It uses
automatic paraphrasing combined with template-based parsing to find alternative
expressions of an attribute in different parts of speech. It also uses a novel
filtered auto-paraphraser to generate correct paraphrases of entire sentences.
We apply AutoQA to the Schema2QA dataset and obtain an average logical form
accuracy of 62.9% when tested on natural questions, which is only 6.4% lower
than a model trained with expert natural language annotations and paraphrase
data collected from crowdworkers. To demonstrate the generality of AutoQA, we
also apply it to the Overnight dataset. AutoQA achieves 69.8% answer accuracy,
16.4% higher than the state-of-the-art zero-shot models and only 5.2% lower
than the same model trained with human data.
| 2,021 | Computation and Language |
On Task-Level Dialogue Composition of Generative Transformer Model | Task-oriented dialogue systems help users accomplish tasks such as booking a
movie ticket and ordering food via conversation. Generative models
parameterized by a deep neural network are widely used for next turn response
generation in such systems. It is natural for users of the system to want to
accomplish multiple tasks within the same conversation, but the ability of
generative models to compose multiple tasks is not well studied. In this work,
we begin by studying the effect of training human-human task-oriented dialogues
towards improving the ability to compose multiple tasks on Transformer
generative models. To that end, we propose and explore two solutions: (1)
creating synthetic multiple task dialogue data for training from human-human
single task dialogue and (2) forcing the encoder representation to be invariant
to single and multiple task dialogues using an auxiliary loss. The results from
our experiments highlight the difficulty of even the sophisticated variant of
transformer model in learning to compose multiple tasks from single task
dialogues.
| 2,020 | Computation and Language |
Relation Classification as Two-way Span-Prediction | The current supervised relation classification (RC) task uses a single
embedding to represent the relation between a pair of entities. We argue that a
better approach is to treat the RC task as span-prediction (SP) problem,
similar to Question answering (QA). We present a span-prediction based system
for RC and evaluate its performance compared to the embedding based system. We
demonstrate that the supervised SP objective works significantly better then
the standard classification based objective. We achieve state-of-the-art
results on the TACRED and SemEval task 8 datasets.
| 2,021 | Computation and Language |
How well does surprisal explain N400 amplitude under different
experimental conditions? | We investigate the extent to which word surprisal can be used to predict a
neural measure of human language processing difficulty - the N400. To do this,
we use recurrent neural networks to calculate the surprisal of stimuli from
previously published neurolinguistic studies of the N400. We find that
surprisal can predict N400 amplitude in a wide range of cases, and the cases
where it cannot do so provide valuable insight into the neurocognitive
processes underlying the response.
| 2,020 | Computation and Language |
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph
Completion | Many graph embedding approaches have been proposed for knowledge graph
completion via link prediction. Among those, translating embedding approaches
enjoy the advantages of light-weight structure, high efficiency and great
interpretability. Especially when extended to complex vector space, they show
the capability in handling various relation patterns including symmetry,
antisymmetry, inversion and composition. However, previous translating
embedding approaches defined in complex vector space suffer from two main
issues: 1) representing and modeling capacities of the model are limited by the
translation function with rigorous multiplication of two complex numbers; and
2) embedding ambiguity caused by one-to-many relations is not explicitly
alleviated. In this paper, we propose a relation-adaptive translation function
built upon a novel weighted product in complex space, where the weights are
learnable, relation-specific and independent to embedding size. The translation
function only requires eight more scalar parameters each relation, but improves
expressive power and alleviates embedding ambiguity problem. Based on the
function, we then present our Relation-adaptive translating Embedding (RatE)
approach to score each graph triple. Moreover, a novel negative sampling method
is proposed to utilize both prior knowledge and self-adversarial learning for
effective optimization. Experiments verify RatE achieves state-of-the-art
performance on four link prediction benchmarks.
| 2,020 | Computation and Language |
Self-play for Data Efficient Language Acquisition | When communicating, people behave consistently across conversational roles:
People understand the words they say and are able to produce the words they
hear. To date, artificial agents developed for language tasks have lacked such
symmetry, meaning agents trained to produce language are unable to understand
it and vice-versa. In this work, we exploit the symmetric nature of
communication in order to improve both the efficiency and quality of language
acquisition in learning agents. Specifically, we consider the setting in which
an agent must learn to both understand and generate words in an existing
language, but with the assumption that access to interaction with "oracle"
speakers of the language is very limited. We show that using self-play as a
substitute for direct supervision enables the agent to transfer its knowledge
across roles (e.g. training as a listener but testing as a speaker) and make
better inferences about the ground truth lexicon using only a handful of
interactions with the oracle.
| 2,020 | Computation and Language |
Adversarial Self-Supervised Data-Free Distillation for Text
Classification | Large pre-trained transformer-based language models have achieved impressive
results on a wide range of NLP tasks. In the past few years, Knowledge
Distillation(KD) has become a popular paradigm to compress a computationally
expensive model to a resource-efficient lightweight model. However, most KD
algorithms, especially in NLP, rely on the accessibility of the original
training dataset, which may be unavailable due to privacy issues. To tackle
this problem, we propose a novel two-stage data-free distillation method, named
Adversarial self-Supervised Data-Free Distillation (AS-DFD), which is designed
for compressing large-scale transformer-based models (e.g., BERT). To avoid
text generation in discrete space, we introduce a Plug & Play Embedding
Guessing method to craft pseudo embeddings from the teacher's hidden knowledge.
Meanwhile, with a self-supervised module to quantify the student's ability, we
adapt the difficulty of pseudo embeddings in an adversarial training manner. To
the best of our knowledge, our framework is the first data-free distillation
framework designed for NLP tasks. We verify the effectiveness of our method on
several text classification datasets.
| 2,020 | Computation and Language |
Discourse structure interacts with reference but not syntax in neural
language models | Language models (LMs) trained on large quantities of text have been claimed
to acquire abstract linguistic representations. Our work tests the robustness
of these abstractions by focusing on the ability of LMs to learn interactions
between different linguistic representations. In particular, we utilized
stimuli from psycholinguistic studies showing that humans can condition
reference (i.e. coreference resolution) and syntactic processing on the same
discourse structure (implicit causality). We compared both transformer and long
short-term memory LMs to find that, contrary to humans, implicit causality only
influences LM behavior for reference, not syntax, despite model representations
that encode the necessary discourse information. Our results further suggest
that LM behavior can contradict not only learned representations of discourse
but also syntactic agreement, pointing to shortcomings of standard language
modeling.
| 2,020 | Computation and Language |
Information Extraction from Swedish Medical Prescriptions with
Sig-Transformer Encoder | Relying on large pretrained language models such as Bidirectional Encoder
Representations from Transformers (BERT) for encoding and adding a simple
prediction layer has led to impressive performance in many clinical natural
language processing (NLP) tasks. In this work, we present a novel extension to
the Transformer architecture, by incorporating signature transform with the
self-attention model. This architecture is added between embedding and
prediction layers. Experiments on a new Swedish prescription data show the
proposed architecture to be superior in two of the three information extraction
tasks, comparing to baseline models. Finally, we evaluate two different
embedding approaches between applying Multilingual BERT and translating the
Swedish text to English then encode with a BERT model pretrained on clinical
notes.
| 2,020 | Computation and Language |
Toward Micro-Dialect Identification in Diaglossic and Code-Switched
Environments | Although the prediction of dialects is an important language processing task,
with a wide range of applications, existing work is largely limited to
coarse-grained varieties. Inspired by geolocation research, we propose the
novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new
language model with striking abilities to predict a fine-grained variety (as
small as that of a city) given a single, short message. For modeling, we offer
a range of novel spatially and linguistically-motivated multi-task learning
models. To showcase the utility of our models, we introduce a new, large-scale
dataset of Arabic micro-varieties (low-resource) suited to our tasks. MARBERT
predicts micro-dialects with 9.9% F1, ~76X better than a majority class
baseline. Our new language model also establishes new state-of-the-art on
several external tasks.
| 2,020 | Computation and Language |
What Do Position Embeddings Learn? An Empirical Study of Pre-Trained
Language Model Positional Encoding | In recent years, pre-trained Transformers have dominated the majority of NLP
benchmark tasks. Many variants of pre-trained Transformers have kept breaking
out, and most focus on designing different pre-training objectives or variants
of self-attention. Embedding the position information in the self-attention
mechanism is also an indispensable factor in Transformers however is often
discussed at will. Therefore, this paper carries out an empirical study on
position embeddings of mainstream pre-trained Transformers, which mainly
focuses on two questions: 1) Do position embeddings really learn the meaning of
positions? 2) How do these different learned position embeddings affect
Transformers for NLP tasks? This paper focuses on providing a new insight of
pre-trained position embeddings through feature-level analysis and empirical
experiments on most of iconic NLP tasks. It is believed that our experimental
results can guide the future work to choose the suitable positional encoding
function for specific tasks given the application property.
| 2,020 | Computation and Language |
Structured Self-Attention Weights Encode Semantics in Sentiment Analysis | Neural attention, especially the self-attention made popular by the
Transformer, has become the workhorse of state-of-the-art natural language
processing (NLP) models. Very recent work suggests that the self-attention in
the Transformer encodes syntactic information; Here, we show that
self-attention scores encode semantics by considering sentiment analysis tasks.
In contrast to gradient-based feature attribution methods, we propose a simple
and effective Layer-wise Attention Tracing (LAT) method to analyze structured
attention weights. We apply our method to Transformer models trained on two
tasks that have surface dissimilarities, but share common semantics---sentiment
analysis of movie reviews and time-series valence prediction in life story
narratives. Across both tasks, words with high aggregated attention weights
were rich in emotional semantics, as quantitatively validated by an emotion
lexicon labeled by human annotators. Our results show that structured attention
weights encode rich semantics in sentiment analysis, and match human
interpretations of semantics.
| 2,020 | Computation and Language |
On Long-Tailed Phenomena in Neural Machine Translation | State-of-the-art Neural Machine Translation (NMT) models struggle with
generating low-frequency tokens, tackling which remains a major challenge. The
analysis of long-tailed phenomena in the context of structured prediction tasks
is further hindered by the added complexities of search during inference. In
this work, we quantitatively characterize such long-tailed phenomena at two
levels of abstraction, namely, token classification and sequence generation. We
propose a new loss function, the Anti-Focal loss, to better adapt model
training to the structural dependencies of conditional text generation by
incorporating the inductive biases of beam search in the training process. We
show the efficacy of the proposed technique on a number of Machine Translation
(MT) datasets, demonstrating that it leads to significant gains over
cross-entropy across different language pairs, especially on the generation of
low-frequency words. We have released the code to reproduce our results.
| 2,020 | Computation and Language |
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce? | Recent latent tree learning models can learn constituency parsing without any
exposure to human-annotated tree structures. One such model is ON-LSTM (Shen et
al., 2019), which is trained on language modelling and has
near-state-of-the-art performance on unsupervised parsing. In order to better
understand the performance and consistency of the model as well as how the
parses it generates are different from gold-standard PTB parses, we replicate
the model with different restarts and examine their parses. We find that (1)
the model has reasonably consistent parsing behaviors across different
restarts, (2) the model struggles with the internal structures of complex noun
phrases, (3) the model has a tendency to overestimate the height of the split
points right before verbs. We speculate that both problems could potentially be
solved by adopting a different training task other than unidirectional language
modelling.
| 2,020 | Computation and Language |
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary | Open-domain response generation is the task of generating sensible and
informative re-sponses to the source sentence. However, neural models tend to
generate safe and mean-ingless responses. While cue-word introducing approaches
encourage responses with concrete semantics and have shown tremendous
potential, they still fail to explore di-verse responses during decoding. In
this paper, we propose a novel but natural approach that can produce multiple
cue-words during decoding, and then uses the produced cue-words to drive
decoding and shrinks the decoding vocabulary. Thus the neural genera-tion model
can explore the full space of responses and discover informative ones with
efficiency. Experimental results show that our approach significantly
outperforms several strong baseline models with much lower decoding complexity.
Especially, our approach can converge to concrete semantics more efficiently
during decoding.
| 2,020 | Computation and Language |
HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for
Sentiment Analysis of Code-Mixed Text | It is fairly common to use code-mixing on a social media platform to express
opinions and emotions in multilingual societies. The purpose of this task is to
detect the sentiment of code-mixed social media text. Code-mixed text poses a
great challenge for the traditional NLP system, which currently uses
monolingual resources to deal with the problem of multilingual mixing. This
task has been solved in the past using lexicon lookup in respective sentiment
dictionaries and using a long short-term memory (LSTM) neural network for
monolingual resources. In this paper, we (my codalab username is kongjun)
present a system that uses a bilingual vector gating mechanism for bilingual
resources to complete the task. The model consists of two main parts: the
vector gating mechanism, which combines the character and word levels, and the
attention mechanism, which extracts the important emotional parts of the text.
The results show that the proposed system outperforms the baseline algorithm.
We achieved fifth place in Spanglish and 19th place in Hinglish.The code of
this paper is availabled at : https://github.com/JunKong5/Semveal2020-task9
| 2,020 | Computation and Language |
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus
with Distributional Models | We address hypernymy detection, i.e., whether an is-a relationship exists
between words (x, y), with the help of large textual corpora. Most conventional
approaches to this task have been categorized to be either pattern-based or
distributional. Recent studies suggest that pattern-based ones are superior, if
large-scale Hearst pairs are extracted and fed, with the sparsity of unseen (x,
y) pairs relieved. However, they become invalid in some specific sparsity
cases, where x or y is not involved in any pattern. For the first time, this
paper quantifies the non-negligible existence of those specific cases. We also
demonstrate that distributional methods are ideal to make up for pattern-based
ones in such cases. We devise a complementary framework, under which a
pattern-based and a distributional model collaborate seamlessly in cases which
they each prefer. On several benchmark datasets, our framework achieves
competitive improvements and the case study shows its better interpretability.
| 2,020 | Computation and Language |
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for
Answer Selection | As conventional answer selection (AS) methods generally match the question
with each candidate answer independently, they suffer from the lack of matching
information between the question and the candidate. To address this problem, we
propose a novel reinforcement learning (RL) based multi-step ranking model,
named MS-Ranker, which accumulates information from potentially correct
candidate answers as extra evidence for matching the question with a candidate.
In specific, we explicitly consider the potential correctness of candidates and
update the evidence with a gating mechanism. Moreover, as we use a listwise
ranking reward, our model learns to pay more attention to the overall
performance. Experiments on two benchmarks, namely WikiQA and SemEval-2016 CQA,
show that our model significantly outperforms existing methods that do not rely
on external resources.
| 2,020 | Computation and Language |
Tag Recommendation for Online Q&A Communities based on BERT Pre-Training
Technique | Online Q&A and open source communities use tags and keywords to index,
categorize, and search for specific content. The most obvious advantage of tag
recommendation is the correct classification of information. In this study, we
used the BERT pre-training technique in tag recommendation task for online Q&A
and open-source communities for the first time. Our evaluation on freecode
datasets show that the proposed method, called TagBERT, is more accurate
compared to deep learning and other baseline methods. Moreover, our model
achieved a high stability by solving the problem of previous researches, where
increasing the number of tag recommendations significantly reduced model
performance.
| 2,021 | Computation and Language |
Can RNNs trained on harder subject-verb agreement instances still
perform well on easier ones? | Previous work suggests that RNNs trained on natural language corpora can
capture number agreement well for simple sentences but perform less well when
sentences contain agreement attractors: intervening nouns between the verb and
the main subject with grammatical number opposite to the latter. This suggests
these models may not learn the actual syntax of agreement, but rather infer
shallower heuristics such as `agree with the recent noun'. In this work, we
investigate RNN models with varying inductive biases trained on selectively
chosen `hard' agreement instances, i.e., sentences with at least one agreement
attractor. For these the verb number cannot be predicted using a simple linear
heuristic, and hence they might help provide the model additional cues for
hierarchical syntax. If RNNs can learn the underlying agreement rules when
trained on such hard instances, then they should generalize well to other
sentences, including simpler ones. However, we observe that several RNN types,
including the ONLSTM which has a soft structural inductive bias, surprisingly
fail to perform well on sentences without attractors when trained solely on
sentences with attractors. We analyze how these selectively trained RNNs
compare to the baseline (training on a natural distribution of agreement
attractors) along the dimensions of number agreement accuracy, representational
similarity, and performance across different syntactic constructions. Our
findings suggest that RNNs trained on our hard agreement instances still do not
capture the underlying syntax of agreement, but rather tend to overfit the
training distribution in a way which leads them to perform poorly on `easy'
out-of-distribution instances. Thus, while RNNs are powerful models which can
pick up non-trivial dependency patterns, inducing them to do so at the level of
syntax rather than surface remains a challenge.
| 2,021 | Computation and Language |
An Empirical Investigation of Beam-Aware Training in Supertagging | Structured prediction is often approached by training a locally normalized
model with maximum likelihood and decoding approximately with beam search. This
approach leads to mismatches as, during training, the model is not exposed to
its mistakes and does not use beam search. Beam-aware training aims to address
these problems, but unfortunately, it is not yet widely used due to a lack of
understanding about how it impacts performance, when it is most useful, and
whether it is stable. Recently, Negrinho et al. (2018) proposed a
meta-algorithm that captures beam-aware training algorithms and suggests new
ones, but unfortunately did not provide empirical results. In this paper, we
begin an empirical investigation: we train the supertagging model of Vaswani et
al. (2016) and a simpler model with instantiations of the meta-algorithm. We
explore the influence of various design choices and make recommendations for
choosing them. We observe that beam-aware training improves performance for
both models, with large improvements for the simpler model which must
effectively manage uncertainty during decoding. Our results suggest that a
model must be learned with search to maximize its effectiveness.
| 2,020 | Computation and Language |
FIND: Human-in-the-Loop Debugging Deep Text Classifiers | Since obtaining a perfect training dataset (i.e., a dataset which is
considerably large, unbiased, and well-representative of unseen cases) is
hardly possible, many real-world text classifiers are trained on the available,
yet imperfect, datasets. These classifiers are thus likely to have undesirable
properties. For instance, they may have biases against some sub-populations or
may not work effectively in the wild due to overfitting. In this paper, we
propose FIND -- a framework which enables humans to debug deep learning text
classifiers by disabling irrelevant hidden features. Experiments show that by
using FIND, humans can improve CNN text classifiers which were trained under
different types of imperfect datasets (including datasets with biases and
datasets with dissimilar train-test distributions).
| 2,020 | Computation and Language |
Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual
Patterns | This paper describes our submission of the WMT 2020 Shared Task on Sentence
Level Direct Assessment, Quality Estimation (QE). In this study, we empirically
reveal the \textit{mismatching issue} when directly adopting BERTScore to QE.
Specifically, there exist lots of mismatching errors between the source
sentence and translated candidate sentence with token pairwise similarity. In
response to this issue, we propose to expose explicit cross-lingual patterns,
\textit{e.g.} word alignments and generation score, to our proposed zero-shot
models. Experiments show that our proposed QE model with explicit cross-lingual
patterns could alleviate the mismatching issue, thereby improving the
performance. Encouragingly, our zero-shot QE method could achieve comparable
performance with supervised QE method, and even outperforms the supervised
counterpart on 2 out of 6 directions. We expect our work could shed light on
the zero-shot QE model improvement.
| 2,020 | Computation and Language |
Beyond Language: Learning Commonsense from Images for Reasoning | This paper proposes a novel approach to learn commonsense from images,
instead of limited raw texts or costly constructed knowledge bases, for the
commonsense reasoning problem in NLP. Our motivation comes from the fact that
an image is worth a thousand words, where richer scene information could be
leveraged to help distill the commonsense knowledge, which is often hidden in
languages. Our approach, namely Loire, consists of two stages. In the first
stage, a bi-modal sequence-to-sequence approach is utilized to conduct the
scene layout generation task, based on a text representation model ViBERT. In
this way, the required visual scene knowledge, such as spatial relations, will
be encoded in ViBERT by the supervised learning process with some bi-modal data
like COCO. Then ViBERT is concatenated with a pre-trained language model to
perform the downstream commonsense reasoning tasks. Experimental results on two
commonsense reasoning problems, i.e. commonsense question answering and pronoun
resolution, demonstrate that Loire outperforms traditional language-based
methods. We also give some case studies to show what knowledge is learned from
images and explain how the generated scene layout helps the commonsense
reasoning process.
| 2,020 | Computation and Language |
Compressing Transformer-Based Semantic Parsing Models using
Compositional Code Embeddings | The current state-of-the-art task-oriented semantic parsing models use BERT
or RoBERTa as pretrained encoders; these models have huge memory footprints.
This poses a challenge to their deployment for voice assistants such as Amazon
Alexa and Google Assistant on edge devices with limited memory budgets. We
propose to learn compositional code embeddings to greatly reduce the sizes of
BERT-base and RoBERTa-base. We also apply the technique to DistilBERT,
ALBERT-base, and ALBERT-large, three already compressed BERT variants which
attain similar state-of-the-art performances on semantic parsing with much
smaller model sizes. We observe 95.15% ~ 98.46% embedding compression rates and
20.47% ~ 34.22% encoder compression rates, while preserving greater than 97.5%
semantic parsing performances. We provide the recipe for training and analyze
the trade-off between code embedding sizes and downstream performances.
| 2,020 | Computation and Language |
Second-Order Neural Dependency Parsing with Message Passing and
End-to-End Training | In this paper, we propose second-order graph-based neural dependency parsing
using message passing and end-to-end neural networks. We empirically show that
our approaches match the accuracy of very recent state-of-the-art second-order
graph-based neural dependency parsers and have significantly faster speed in
both training and testing. We also empirically show the advantage of
second-order parsing over first-order parsing and observe that the usefulness
of the head-selection structured constraint vanishes when using BERT embedding.
| 2,021 | Computation and Language |
Automated Concatenation of Embeddings for Structured Prediction | Pretrained contextualized embeddings are powerful word representations for
structured prediction tasks. Recent work found that better word representations
can be obtained by concatenating different types of embeddings. However, the
selection of embeddings to form the best concatenated representation usually
varies depending on the task and the collection of candidate embeddings, and
the ever-increasing number of embedding types makes it a more difficult
problem. In this paper, we propose Automated Concatenation of Embeddings (ACE)
to automate the process of finding better concatenations of embeddings for
structured prediction tasks, based on a formulation inspired by recent progress
on neural architecture search. Specifically, a controller alternately samples a
concatenation of embeddings, according to its current belief of the
effectiveness of individual embedding types in consideration for a task, and
updates the belief based on a reward. We follow strategies in reinforcement
learning to optimize the parameters of the controller and compute the reward
based on the accuracy of a task model, which is fed with the sampled
concatenation as input and trained on a task dataset. Empirical results on 6
tasks and 21 datasets show that our approach outperforms strong baselines and
achieves state-of-the-art performance with fine-tuned embeddings in all the
evaluations.
| 2,021 | Computation and Language |
Structural Knowledge Distillation: Tractably Distilling Information for
Structured Predictor | Knowledge distillation is a critical technique to transfer knowledge between
models, typically from a large model (the teacher) to a more fine-grained one
(the student). The objective function of knowledge distillation is typically
the cross-entropy between the teacher and the student's output distributions.
However, for structured prediction problems, the output space is exponential in
size; therefore, the cross-entropy objective becomes intractable to compute and
optimize directly. In this paper, we derive a factorized form of the knowledge
distillation objective for structured prediction, which is tractable for many
typical choices of the teacher and student models. In particular, we show the
tractability and empirical effectiveness of structural knowledge distillation
between sequence labeling and dependency parsing models under four different
scenarios: 1) the teacher and student share the same factorization form of the
output structure scoring function; 2) the student factorization produces more
fine-grained substructures than the teacher factorization; 3) the teacher
factorization produces more fine-grained substructures than the student
factorization; 4) the factorization forms from the teacher and the student are
incompatible.
| 2,021 | Computation and Language |
Semi-supervised Formality Style Transfer using Language Model
Discriminator and Mutual Information Maximization | Formality style transfer is the task of converting informal sentences to
grammatically-correct formal sentences, which can be used to improve
performance of many downstream NLP tasks. In this work, we propose a
semi-supervised formality style transfer model that utilizes a language
model-based discriminator to maximize the likelihood of the output sentence
being formal, which allows us to use maximization of token-level conditional
probabilities for training. We further propose to maximize mutual information
between source and target styles as our training objective instead of
maximizing the regular likelihood that often leads to repetitive and trivial
generated responses. Experiments showed that our model outperformed previous
state-of-the-art baselines significantly in terms of both automated metrics and
human judgement. We further generalized our model to unsupervised text style
transfer task, and achieved significant improvements on two benchmark sentiment
style transfer datasets.
| 2,020 | Computation and Language |
Leveraging Spatial Information in Radiology Reports for Ischemic Stroke
Phenotyping | Classifying fine-grained ischemic stroke phenotypes relies on identifying
important clinical information. Radiology reports provide relevant information
with context to determine such phenotype information. We focus on stroke
phenotypes with location-specific information: brain region affected,
laterality, stroke stage, and lacunarity. We use an existing fine-grained
spatial information extraction system--Rad-SpatialNet--to identify clinically
important information and apply simple domain rules on the extracted
information to classify phenotypes. The performance of our proposed approach is
promising (recall of 89.62% for classifying brain region and 74.11% for
classifying brain region, side, and stroke stage together). Our work
demonstrates that an information extraction system based on a fine-grained
schema can be utilized to determine complex phenotypes with the inclusion of
simple domain rules. These phenotypes have the potential to facilitate stroke
research focusing on post-stroke outcome and treatment planning based on the
stroke location.
| 2,020 | Computation and Language |
On the Importance of Adaptive Data Collection for Extremely Imbalanced
Pairwise Tasks | Many pairwise classification tasks, such as paraphrase detection and
open-domain question answering, naturally have extreme label imbalance (e.g.,
$99.99\%$ of examples are negatives). In contrast, many recent datasets
heuristically choose examples to ensure label balance. We show that these
heuristics lead to trained models that generalize poorly: State-of-the art
models trained on QQP and WikiQA each have only $2.4\%$ average precision when
evaluated on realistically imbalanced test data. We instead collect training
data with active learning, using a BERT-based embedding model to efficiently
retrieve uncertain points from a very large pool of unlabeled utterance pairs.
By creating balanced training data with more informative negative examples,
active learning greatly improves average precision to $32.5\%$ on QQP and
$20.1\%$ on WikiQA.
| 2,020 | Computation and Language |
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