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Viable Threat on News Reading: Generating Biased News Using Natural
Language Models | Recent advancements in natural language generation has raised serious
concerns. High-performance language models are widely used for language
generation tasks because they are able to produce fluent and meaningful
sentences. These models are already being used to create fake news. They can
also be exploited to generate biased news, which can then be used to attack
news aggregators to change their reader's behavior and influence their bias. In
this paper, we use a threat model to demonstrate that the publicly available
language models can reliably generate biased news content based on an input
original news. We also show that a large number of high-quality biased news
articles can be generated using controllable text generation. A subjective
evaluation with 80 participants demonstrated that the generated biased news is
generally fluent, and a bias evaluation with 24 participants demonstrated that
the bias (left or right) is usually evident in the generated articles and can
be easily identified.
| 2,020 | Computation and Language |
Knowledge Association with Hyperbolic Knowledge Graph Embeddings | Capturing associations for knowledge graphs (KGs) through entity alignment,
entity type inference and other related tasks benefits NLP applications with
comprehensive knowledge representations. Recent related methods built on
Euclidean embeddings are challenged by the hierarchical structures and
different scales of KGs. They also depend on high embedding dimensions to
realize enough expressiveness. Differently, we explore with low-dimensional
hyperbolic embeddings for knowledge association. We propose a hyperbolic
relational graph neural network for KG embedding and capture knowledge
associations with a hyperbolic transformation. Extensive experiments on entity
alignment and type inference demonstrate the effectiveness and efficiency of
our method.
| 2,020 | Computation and Language |
A Streaming Approach For Efficient Batched Beam Search | We propose an efficient batching strategy for variable-length decoding on GPU
architectures. During decoding, when candidates terminate or are pruned
according to heuristics, our streaming approach periodically "refills" the
batch before proceeding with a selected subset of candidates. We apply our
method to variable-width beam search on a state-of-the-art machine translation
model. Our method decreases runtime by up to 71% compared to a fixed-width beam
search baseline and 17% compared to a variable-width baseline, while matching
baselines' BLEU. Finally, experiments show that our method can speed up
decoding in other domains, such as semantic and syntactic parsing.
| 2,021 | Computation and Language |
Speakers Fill Lexical Semantic Gaps with Context | Lexical ambiguity is widespread in language, allowing for the reuse of
economical word forms and therefore making language more efficient. If
ambiguous words cannot be disambiguated from context, however, this gain in
efficiency might make language less clear -- resulting in frequent
miscommunication. For a language to be clear and efficiently encoded, we posit
that the lexical ambiguity of a word type should correlate with how much
information context provides about it, on average. To investigate whether this
is the case, we operationalise the lexical ambiguity of a word as the entropy
of meanings it can take, and provide two ways to estimate this -- one which
requires human annotation (using WordNet), and one which does not (using BERT),
making it readily applicable to a large number of languages. We validate these
measures by showing that, on six high-resource languages, there are significant
Pearson correlations between our BERT-based estimate of ambiguity and the
number of synonyms a word has in WordNet (e.g. $\rho = 0.40$ in English). We
then test our main hypothesis -- that a word's lexical ambiguity should
negatively correlate with its contextual uncertainty -- and find significant
correlations on all 18 typologically diverse languages we analyse. This
suggests that, in the presence of ambiguity, speakers compensate by making
contexts more informative.
| 2,021 | Computation and Language |
Assessing the Helpfulness of Learning Materials with Inference-Based
Learner-Like Agent | Many English-as-a-second language learners have trouble using near-synonym
words (e.g., small vs.little; briefly vs.shortly) correctly, and often look for
example sentences to learn how two nearly synonymous terms differ. Prior work
uses hand-crafted scores to recommend sentences but has difficulty in adopting
such scores to all the near-synonyms as near-synonyms differ in various ways.
We notice that the helpfulness of the learning material would reflect on the
learners' performance. Thus, we propose the inference-based learner-like agent
to mimic learner behavior and identify good learning materials by examining the
agent's performance. To enable the agent to behave like a learner, we leverage
entailment modeling's capability of inferring answers from the provided
materials. Experimental results show that the proposed agent is equipped with
good learner-like behavior to achieve the best performance in both
fill-in-the-blank (FITB) and good example sentence selection tasks. We further
conduct a classroom user study with college ESL learners. The results of the
user study show that the proposed agent can find out example sentences that
help students learn more easily and efficiently. Compared to other models, the
proposed agent improves the score of more than 17% of students after learning.
| 2,020 | Computation and Language |
Pareto Probing: Trading Off Accuracy for Complexity | The question of how to probe contextual word representations for linguistic
structure in a way that is both principled and useful has seen significant
attention recently in the NLP literature. In our contribution to this
discussion, we argue for a probe metric that reflects the fundamental trade-off
between probe complexity and performance: the Pareto hypervolume. To measure
complexity, we present a number of parametric and non-parametric metrics. Our
experiments using Pareto hypervolume as an evaluation metric show that probes
often do not conform to our expectations -- e.g., why should the non-contextual
fastText representations encode more morpho-syntactic information than the
contextual BERT representations? These results suggest that common, simplistic
probing tasks, such as part-of-speech labeling and dependency arc labeling, are
inadequate to evaluate the linguistic structure encoded in contextual word
representations. This leads us to propose full dependency parsing as a probing
task. In support of our suggestion that harder probing tasks are necessary, our
experiments with dependency parsing reveal a wide gap in syntactic knowledge
between contextual and non-contextual representations.
| 2,023 | Computation and Language |
Self-training Improves Pre-training for Natural Language Understanding | Unsupervised pre-training has led to much recent progress in natural language
understanding. In this paper, we study self-training as another way to leverage
unlabeled data through semi-supervised learning. To obtain additional data for
a specific task, we introduce SentAugment, a data augmentation method which
computes task-specific query embeddings from labeled data to retrieve sentences
from a bank of billions of unlabeled sentences crawled from the web. Unlike
previous semi-supervised methods, our approach does not require in-domain
unlabeled data and is therefore more generally applicable. Experiments show
that self-training is complementary to strong RoBERTa baselines on a variety of
tasks. Our augmentation approach leads to scalable and effective self-training
with improvements of up to 2.6% on standard text classification benchmarks.
Finally, we also show strong gains on knowledge-distillation and few-shot
learning.
| 2,020 | Computation and Language |
Learning to Generalize for Sequential Decision Making | We consider problems of making sequences of decisions to accomplish tasks,
interacting via the medium of language. These problems are often tackled with
reinforcement learning approaches. We find that these models do not generalize
well when applied to novel task domains. However, the large amount of
computation necessary to adequately train and explore the search space of
sequential decision making, under a reinforcement learning paradigm, precludes
the inclusion of large contextualized language models, which might otherwise
enable the desired generalization ability. We introduce a teacher-student
imitation learning methodology and a means of converting a reinforcement
learning model into a natural language understanding model. Together, these
methodologies enable the introduction of contextualized language models into
the sequential decision making problem space. We show that models can learn
faster and generalize more, leveraging both the imitation learning and the
reformulation. Our models exceed teacher performance on various held-out
decision problems, by up to 7% on in-domain problems and 24% on out-of-domain
problems.
| 2,020 | Computation and Language |
Acrostic Poem Generation | We propose a new task in the area of computational creativity: acrostic poem
generation in English. Acrostic poems are poems that contain a hidden message;
typically, the first letter of each line spells out a word or short phrase. We
define the task as a generation task with multiple constraints: given an input
word, 1) the initial letters of each line should spell out the provided word,
2) the poem's semantics should also relate to it, and 3) the poem should
conform to a rhyming scheme. We further provide a baseline model for the task,
which consists of a conditional neural language model in combination with a
neural rhyming model. Since no dedicated datasets for acrostic poem generation
exist, we create training data for our task by first training a separate topic
prediction model on a small set of topic-annotated poems and then predicting
topics for additional poems. Our experiments show that the acrostic poems
generated by our baseline are received well by humans and do not lose much
quality due to the additional constraints. Last, we confirm that poems
generated by our model are indeed closely related to the provided prompts, and
that pretraining on Wikipedia can boost performance.
| 2,020 | Computation and Language |
MedFilter: Improving Extraction of Task-relevant Utterances from
Doctor-Patient Conversations through Integration of Discourse Structure and
Ontological Knowledge | Information extraction from conversational data is particularly challenging
because the task-centric nature of conversation allows for effective
communication of implicit information by humans, but is challenging for
machines. The challenges may differ between utterances depending on the role of
the speaker within the conversation, especially when relevant expertise is
distributed asymmetrically across roles. Further, the challenges may also
increase over the conversation as more shared context is built up through
information communicated implicitly earlier in the dialogue. In this paper, we
propose the novel modeling approach MedFilter, which addresses these insights
in order to increase performance at identifying and categorizing task-relevant
utterances, and in so doing, positively impacts performance at a downstream
information extraction task. We evaluate this approach on a corpus of nearly
7,000 doctor-patient conversations where MedFilter is used to identify
medically relevant contributions to the discussion (achieving a 10% improvement
over SOTA baselines in terms of area under the PR curve). Identifying
task-relevant utterances benefits downstream medical processing, achieving
improvements of 15%, 105%, and 23% respectively for the extraction of symptoms,
medications, and complaints.
| 2,022 | Computation and Language |
An Ensemble Approach for Automatic Structuring of Radiology Reports | Automatic structuring of electronic medical records is of high demand for
clinical workflow solutions to facilitate extraction, storage, and querying of
patient care information. However, developing a scalable solution is extremely
challenging, specifically for radiology reports, as most healthcare institutes
use either no template or department/institute specific templates. Moreover,
radiologists' reporting style varies from one to another as sentences are
telegraphic and do not follow general English grammar rules. We present an
ensemble method that consolidates the predictions of three models, capturing
various attributes of textual information for automatic labeling of sentences
with section labels. These three models are: 1) Focus Sentence model, capturing
context of the target sentence; 2) Surrounding Context model, capturing the
neighboring context of the target sentence; and finally, 3) Formatting/Layout
model, aimed at learning report formatting cues. We utilize Bi-directional
LSTMs, followed by sentence encoders, to acquire the context. Furthermore, we
define several features that incorporate the structure of reports. We compare
our proposed approach against multiple baselines and state-of-the-art
approaches on a proprietary dataset as well as 100 manually annotated radiology
notes from the MIMIC-III dataset, which we are making publicly available. Our
proposed approach significantly outperforms other approaches by achieving 97.1%
accuracy.
| 2,020 | Computation and Language |
Effects of Naturalistic Variation in Goal-Oriented Dialog | Existing benchmarks used to evaluate the performance of end-to-end neural
dialog systems lack a key component: natural variation present in human
conversations. Most datasets are constructed through crowdsourcing, where the
crowd workers follow a fixed template of instructions while enacting the role
of a user/agent. This results in straight-forward, somewhat routine, and mostly
trouble-free conversations, as crowd workers do not think to represent the full
range of actions that occur naturally with real users. In this work, we
investigate the impact of naturalistic variation on two goal-oriented datasets:
bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new
and more effective testbeds for both datasets, by introducing naturalistic
variation by the user. We observe that there is a significant drop in
performance (more than 60% in Ent. F1 on SMD and 85% in per-dialog accuracy on
bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet
and GLMP on both datasets.
| 2,020 | Computation and Language |
SPLAT: Speech-Language Joint Pre-Training for Spoken Language
Understanding | Spoken language understanding (SLU) requires a model to analyze input
acoustic signal to understand its linguistic content and make predictions. To
boost the models' performance, various pre-training methods have been proposed
to learn rich representations from large-scale unannotated speech and text.
However, the inherent disparities between the two modalities necessitate a
mutual analysis. In this paper, we propose a novel semi-supervised learning
framework, SPLAT, to jointly pre-train the speech and language modules. Besides
conducting a self-supervised masked language modeling task on the two
individual modules using unpaired speech and text, SPLAT aligns representations
from the two modules in a shared latent space using a small amount of paired
speech and text. Thus, during fine-tuning, the speech module alone can produce
representations carrying both acoustic information and contextual semantic
knowledge of an input acoustic signal. Experimental results verify the
effectiveness of our approach on various SLU tasks. For example, SPLAT improves
the previous state-of-the-art performance on the Spoken SQuAD dataset by more
than 10%.
| 2,021 | Computation and Language |
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for
Long Text Generation | Pre-trained Transformers have enabled impressive breakthroughs in generating
long and fluent text, yet their outputs are often "rambling" without coherently
arranged content. In this work, we present a novel content-controlled text
generation framework, PAIR, with planning and iterative refinement, which is
built upon a large model, BART. We first adapt the BERT model to automatically
construct the content plans, consisting of keyphrase assignments and their
corresponding sentence-level positions. The BART model is employed for
generation without modifying its structure. We then propose a refinement
algorithm to gradually enhance the generation quality within the
sequence-to-sequence framework. Evaluation with automatic metrics shows that
adding planning consistently improves the generation quality on three distinct
domains, with an average of 20 BLEU points and 12 METEOR points improvements.
In addition, human judges rate our system outputs to be more relevant and
coherent than comparisons without planning.
| 2,020 | Computation and Language |
Conversational Document Prediction to Assist Customer Care Agents | A frequent pattern in customer care conversations is the agents responding
with appropriate webpage URLs that address users' needs. We study the task of
predicting the documents that customer care agents can use to facilitate users'
needs. We also introduce a new public dataset which supports the aforementioned
problem. Using this dataset and two others, we investigate state-of-the art
deep learning (DL) and information retrieval (IR) models for the task.
Additionally, we analyze the practicality of such systems in terms of inference
time complexity. Our show that an hybrid IR+DL approach provides the best of
both worlds.
| 2,020 | Computation and Language |
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation | Data-to-text generation has recently attracted substantial interests due to
its wide applications. Existing methods have shown impressive performance on an
array of tasks. However, they rely on a significant amount of labeled data for
each task, which is costly to acquire and thus limits their application to new
tasks and domains. In this paper, we propose to leverage pre-training and
transfer learning to address this issue. We propose a knowledge-grounded
pre-training (KGPT), which consists of two parts, 1) a general
knowledge-grounded generation model to generate knowledge-enriched text. 2) a
pre-training paradigm on a massive knowledge-grounded text corpus crawled from
the web. The pre-trained model can be fine-tuned on various data-to-text
generation tasks to generate task-specific text. We adopt three settings,
namely fully-supervised, zero-shot, few-shot to evaluate its effectiveness.
Under the fully-supervised setting, our model can achieve remarkable gains over
the known baselines. Under zero-shot setting, our model without seeing any
examples achieves over 30 ROUGE-L on WebNLG while all other baselines fail.
Under the few-shot setting, our model only needs about one-fifteenth as many
labeled examples to achieve the same level of performance as baseline models.
These experiments consistently prove the strong generalization ability of our
proposed framework https://github.com/wenhuchen/KGPT.
| 2,020 | Computation and Language |
Sentiment Analysis for Reinforcement Learning | While reinforcement learning (RL) has been successful in natural language
processing (NLP) domains such as dialogue generation and text-based games, it
typically faces the problem of sparse rewards that leads to slow or no
convergence. Traditional methods that use text descriptions to extract only a
state representation ignore the feedback inherently present in them. In
text-based games, for example, descriptions like "Good Job! You ate the food}"
indicate progress, and descriptions like "You entered a new room" indicate
exploration. Positive and negative cues like these can be converted to rewards
through sentiment analysis. This technique converts the sparse reward problem
into a dense one, which is easier to solve. Furthermore, this can enable
reinforcement learning without rewards, in which the agent learns entirely from
these intrinsic sentiment rewards. This framework is similar to intrinsic
motivation, where the environment does not necessarily provide the rewards, but
the agent analyzes and realizes them by itself. We find that providing dense
rewards in text-based games using sentiment analysis improves performance under
some conditions.
| 2,020 | Computation and Language |
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup | Active learning is an important technique for low-resource sequence labeling
tasks. However, current active sequence labeling methods use the queried
samples alone in each iteration, which is an inefficient way of leveraging
human annotations. We propose a simple but effective data augmentation method
to improve the label efficiency of active sequence labeling. Our method,
SeqMix, simply augments the queried samples by generating extra labeled
sequences in each iteration. The key difficulty is to generate plausible
sequences along with token-level labels. In SeqMix, we address this challenge
by performing mixup for both sequences and token-level labels of the queried
samples. Furthermore, we design a discriminator during sequence mixup, which
judges whether the generated sequences are plausible or not. Our experiments on
Named Entity Recognition and Event Detection tasks show that SeqMix can improve
the standard active sequence labeling method by $2.27\%$--$3.75\%$ in terms of
$F_1$ scores. The code and data for SeqMix can be found at
https://github.com/rz-zhang/SeqMix
| 2,020 | Computation and Language |
InfoBERT: Improving Robustness of Language Models from An Information
Theoretic Perspective | Large-scale language models such as BERT have achieved state-of-the-art
performance across a wide range of NLP tasks. Recent studies, however, show
that such BERT-based models are vulnerable facing the threats of textual
adversarial attacks. We aim to address this problem from an
information-theoretic perspective, and propose InfoBERT, a novel learning
framework for robust fine-tuning of pre-trained language models. InfoBERT
contains two mutual-information-based regularizers for model training: (i) an
Information Bottleneck regularizer, which suppresses noisy mutual information
between the input and the feature representation; and (ii) a Robust Feature
regularizer, which increases the mutual information between local robust
features and global features. We provide a principled way to theoretically
analyze and improve the robustness of representation learning for language
models in both standard and adversarial training. Extensive experiments
demonstrate that InfoBERT achieves state-of-the-art robust accuracy over
several adversarial datasets on Natural Language Inference (NLI) and Question
Answering (QA) tasks. Our code is available at
https://github.com/AI-secure/InfoBERT.
| 2,021 | Computation and Language |
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial
Text Generation | NLP models are shown to suffer from robustness issues, i.e., a model's
prediction can be easily changed under small perturbations to the input. In
this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model
that, given an input text, generates adversarial texts through controllable
attributes that are known to be invariant to task labels. For example, in order
to attack a model for sentiment classification over product reviews, we can use
the product categories as the controllable attribute which would not change the
sentiment of the reviews. Experiments on real-world NLP datasets demonstrate
that our method can generate more diverse and fluent adversarial texts,
compared to many existing adversarial text generation approaches. We further
use our generated adversarial examples to improve models through adversarial
training, and we demonstrate that our generated attacks are more robust against
model re-training and different model architectures.
| 2,020 | Computation and Language |
We Don't Speak the Same Language: Interpreting Polarization through
Machine Translation | Polarization among US political parties, media and elites is a widely studied
topic. Prominent lines of prior research across multiple disciplines have
observed and analyzed growing polarization in social media. In this paper, we
present a new methodology that offers a fresh perspective on interpreting
polarization through the lens of machine translation. With a novel proposition
that two sub-communities are speaking in two different \emph{languages}, we
demonstrate that modern machine translation methods can provide a simple yet
powerful and interpretable framework to understand the differences between two
(or more) large-scale social media discussion data sets at the granularity of
words. Via a substantial corpus of 86.6 million comments by 6.5 million users
on over 200,000 news videos hosted by YouTube channels of four prominent US
news networks, we demonstrate that simple word-level and phrase-level
translation pairs can reveal deep insights into the current political divide --
what is \emph{black lives matter} to one can be \emph{all lives matter} to the
other.
| 2,020 | Computation and Language |
Inference Strategies for Machine Translation with Conditional Masking | Conditional masked language model (CMLM) training has proven successful for
non-autoregressive and semi-autoregressive sequence generation tasks, such as
machine translation. Given a trained CMLM, however, it is not clear what the
best inference strategy is. We formulate masked inference as a factorization of
conditional probabilities of partial sequences, show that this does not harm
performance, and investigate a number of simple heuristics motivated by this
perspective. We identify a thresholding strategy that has advantages over the
standard "mask-predict" algorithm, and provide analyses of its behavior on
machine translation tasks.
| 2,020 | Computation and Language |
Participatory Research for Low-resourced Machine Translation: A Case
Study in African Languages | Research in NLP lacks geographic diversity, and the question of how NLP can
be scaled to low-resourced languages has not yet been adequately solved.
"Low-resourced"-ness is a complex problem going beyond data availability and
reflects systemic problems in society. In this paper, we focus on the task of
Machine Translation (MT), that plays a crucial role for information
accessibility and communication worldwide. Despite immense improvements in MT
over the past decade, MT is centered around a few high-resourced languages. As
MT researchers cannot solve the problem of low-resourcedness alone, we propose
participatory research as a means to involve all necessary agents required in
the MT development process. We demonstrate the feasibility and scalability of
participatory research with a case study on MT for African languages. Its
implementation leads to a collection of novel translation datasets, MT
benchmarks for over 30 languages, with human evaluations for a third of them,
and enables participants without formal training to make a unique scientific
contribution. Benchmarks, models, data, code, and evaluation results are
released under https://github.com/masakhane-io/masakhane-mt.
| 2,020 | Computation and Language |
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent
Structure Learning | Latent structure models are a powerful tool for modeling language data: they
can mitigate the error propagation and annotation bottleneck in pipeline
systems, while simultaneously uncovering linguistic insights about the data.
One challenge with end-to-end training of these models is the argmax operation,
which has null gradient. In this paper, we focus on surrogate gradients, a
popular strategy to deal with this problem. We explore latent structure
learning through the angle of pulling back the downstream learning objective.
In this paradigm, we discover a principled motivation for both the
straight-through estimator (STE) as well as the recently-proposed SPIGOT - a
variant of STE for structured models. Our perspective leads to new algorithms
in the same family. We empirically compare the known and the novel pulled-back
estimators against the popular alternatives, yielding new insight for
practitioners and revealing intriguing failure cases.
| 2,020 | Computation and Language |
Investigating representations of verb bias in neural language models | Languages typically provide more than one grammatical construction to express
certain types of messages. A speaker's choice of construction is known to
depend on multiple factors, including the choice of main verb -- a phenomenon
known as \emph{verb bias}. Here we introduce DAIS, a large benchmark dataset
containing 50K human judgments for 5K distinct sentence pairs in the English
dative alternation. This dataset includes 200 unique verbs and systematically
varies the definiteness and length of arguments. We use this dataset, as well
as an existing corpus of naturally occurring data, to evaluate how well recent
neural language models capture human preferences. Results show that larger
models perform better than smaller models, and transformer architectures (e.g.
GPT-2) tend to out-perform recurrent architectures (e.g. LSTMs) even under
comparable parameter and training settings. Additional analyses of internal
feature representations suggest that transformers may better integrate specific
lexical information with grammatical constructions.
| 2,020 | Computation and Language |
Improving Neural Topic Models using Knowledge Distillation | Topic models are often used to identify human-interpretable topics to help
make sense of large document collections. We use knowledge distillation to
combine the best attributes of probabilistic topic models and pretrained
transformers. Our modular method can be straightforwardly applied with any
neural topic model to improve topic quality, which we demonstrate using two
models having disparate architectures, obtaining state-of-the-art topic
coherence. We show that our adaptable framework not only improves performance
in the aggregate over all estimated topics, as is commonly reported, but also
in head-to-head comparisons of aligned topics.
| 2,020 | Computation and Language |
Fine-Grained Grounding for Multimodal Speech Recognition | Multimodal automatic speech recognition systems integrate information from
images to improve speech recognition quality, by grounding the speech in the
visual context. While visual signals have been shown to be useful for
recovering entities that have been masked in the audio, these models should be
capable of recovering a broader range of word types. Existing systems rely on
global visual features that represent the entire image, but localizing the
relevant regions of the image will make it possible to recover a larger set of
words, such as adjectives and verbs. In this paper, we propose a model that
uses finer-grained visual information from different parts of the image, using
automatic object proposals. In experiments on the Flickr8K Audio Captions
Corpus, we find that our model improves over approaches that use global visual
features, that the proposals enable the model to recover entities and other
related words, such as adjectives, and that improvements are due to the model's
ability to localize the correct proposals.
| 2,020 | Computation and Language |
Interactive Fiction Game Playing as Multi-Paragraph Reading
Comprehension with Reinforcement Learning | Interactive Fiction (IF) games with real human-written natural language texts
provide a new natural evaluation for language understanding techniques. In
contrast to previous text games with mostly synthetic texts, IF games pose
language understanding challenges on the human-written textual descriptions of
diverse and sophisticated game worlds and language generation challenges on the
action command generation from less restricted combinatorial space. We take a
novel perspective of IF game solving and re-formulate it as Multi-Passage
Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query
attention mechanisms and the structured prediction in MPRC to efficiently
generate and evaluate action outputs and apply an object-centric historical
observation retrieval strategy to mitigate the partial observability of the
textual observations. Extensive experiments on the recent IF benchmark
(Jericho) demonstrate clear advantages of our approaches achieving high winning
rates and low data requirements compared to all previous approaches. Our source
code is available at: https://github.com/XiaoxiaoGuo/rcdqn.
| 2,020 | Computation and Language |
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks | Mixup is the latest data augmentation technique that linearly interpolates
input examples and the corresponding labels. It has shown strong effectiveness
in image classification by interpolating images at the pixel level. Inspired by
this line of research, in this paper, we explore i) how to apply mixup to
natural language processing tasks since text data can hardly be mixed in the
raw format; ii) if mixup is still effective in transformer-based learning
models, e.g., BERT. To achieve the goal, we incorporate mixup to
transformer-based pre-trained architecture, named "mixup-transformer", for a
wide range of NLP tasks while keeping the whole end-to-end training system. We
evaluate the proposed framework by running extensive experiments on the GLUE
benchmark. Furthermore, we also examine the performance of mixup-transformer in
low-resource scenarios by reducing the training data with a certain ratio. Our
studies show that mixup is a domain-independent data augmentation technique to
pre-trained language models, resulting in significant performance improvement
for transformer-based models.
| 2,020 | Computation and Language |
A Generalized Constraint Approach to Bilingual Dictionary Induction for
Low-Resource Language Families | The lack or absence of parallel and comparable corpora makes bilingual
lexicon extraction a difficult task for low-resource languages. The pivot
language and cognate recognition approaches have been proven useful for
inducing bilingual lexicons for such languages. We propose constraint-based
bilingual lexicon induction for closely-related languages by extending
constraints from the recent pivot-based induction technique and further
enabling multiple symmetry assumption cycles to reach many more cognates in the
transgraph. We further identify cognate synonyms to obtain many-to-many
translation pairs. This paper utilizes four datasets: one Austronesian
low-resource language and three Indo-European high-resource languages. We use
three constraint-based methods from our previous work, the Inverse Consultation
method and translation pairs generated from the Cartesian product of input
dictionaries as baselines. We evaluate our result using the metrics of
precision, recall and F-score. Our customizable approach allows the user to
conduct cross-validation to predict the optimal hyperparameters (cognate
threshold and cognate synonym threshold) with various combinations of
heuristics and the number of symmetry assumption cycles to gain the highest
F-score. Our proposed methods have statistically significant improvement of
precision and F-score compared to our previous constraint-based methods. The
results show that our method demonstrates the potential to complement other
bilingual dictionary creation methods like word alignment models using parallel
corpora for high-resource languages while well handling low-resource languages.
| 2,017 | Computation and Language |
Plan Optimization to Bilingual Dictionary Induction for Low-Resource
Language Families | Creating bilingual dictionary is the first crucial step in enriching
low-resource languages. Especially for the closely-related ones, it has been
shown that the constraint-based approach is useful for inducing bilingual
lexicons from two bilingual dictionaries via the pivot language. However, if
there are no available machine-readable dictionaries as input, we need to
consider manual creation by bilingual native speakers. To reach a goal of
comprehensively create multiple bilingual dictionaries, even if we already have
several existing machine-readable bilingual dictionaries, it is still difficult
to determine the execution order of the constraint-based approach to reducing
the total cost. Plan optimization is crucial in composing the order of
bilingual dictionaries creation with the consideration of the methods and their
costs. We formalize the plan optimization for creating bilingual dictionaries
by utilizing Markov Decision Process (MDP) with the goal to get a more accurate
estimation of the most feasible optimal plan with the least total cost before
fully implementing the constraint-based bilingual lexicon induction. We model a
prior beta distribution of bilingual lexicon induction precision with language
similarity and polysemy of the topology as $\alpha$ and $\beta$ parameters. It
is further used to model cost function and state transition probability. We
estimated the cost of all investment plan as a baseline for evaluating the
proposed MDP-based approach with total cost as an evaluation metric. After
utilizing the posterior beta distribution in the first batch of experiments to
construct the prior beta distribution in the second batch of experiments, the
result shows 61.5\% of cost reduction compared to the estimated all investment
plan and 39.4\% of cost reduction compared to the estimated MDP optimal plan.
The MDP-based proposal outperformed the baseline on the total cost.
| 2,020 | Computation and Language |
Guiding Attention for Self-Supervised Learning with Transformers | In this paper, we propose a simple and effective technique to allow for
efficient self-supervised learning with bi-directional Transformers. Our
approach is motivated by recent studies demonstrating that self-attention
patterns in trained models contain a majority of non-linguistic regularities.
We propose a computationally efficient auxiliary loss function to guide
attention heads to conform to such patterns. Our method is agnostic to the
actual pre-training objective and results in faster convergence of models as
well as better performance on downstream tasks compared to the baselines,
achieving state of the art results in low-resource settings. Surprisingly, we
also find that linguistic properties of attention heads are not necessarily
correlated with language modeling performance.
| 2,020 | Computation and Language |
Simple and Effective Few-Shot Named Entity Recognition with Structured
Nearest Neighbor Learning | We present a simple few-shot named entity recognition (NER) system based on
nearest neighbor learning and structured inference. Our system uses a
supervised NER model trained on the source domain, as a feature extractor.
Across several test domains, we show that a nearest neighbor classifier in this
feature-space is far more effective than the standard meta-learning approaches.
We further propose a cheap but effective method to capture the label
dependencies between entity tags without expensive CRF training. We show that
our method of combining structured decoding with nearest neighbor learning
achieves state-of-the-art performance on standard few-shot NER evaluation
tasks, improving F1 scores by $6\%$ to $16\%$ absolute points over prior
meta-learning based systems.
| 2,020 | Computation and Language |
Adversarial Grammatical Error Correction | Recent works in Grammatical Error Correction (GEC) have leveraged the
progress in Neural Machine Translation (NMT), to learn rewrites from parallel
corpora of grammatically incorrect and corrected sentences, achieving
state-of-the-art results. At the same time, Generative Adversarial Networks
(GANs) have been successful in generating realistic texts across many different
tasks by learning to directly minimize the difference between human-generated
and synthetic text. In this work, we present an adversarial learning approach
to GEC, using the generator-discriminator framework. The generator is a
Transformer model, trained to produce grammatically correct sentences given
grammatically incorrect ones. The discriminator is a sentence-pair
classification model, trained to judge a given pair of grammatically
incorrect-correct sentences on the quality of grammatical correction. We
pre-train both the discriminator and the generator on parallel texts and then
fine-tune them further using a policy gradient method that assigns high rewards
to sentences which could be true corrections of the grammatically incorrect
text. Experimental results on FCE, CoNLL-14, and BEA-19 datasets show that
Adversarial-GEC can achieve competitive GEC quality compared to NMT-based
baselines.
| 2,020 | Computation and Language |
Efficient One-Pass End-to-End Entity Linking for Questions | We present ELQ, a fast end-to-end entity linking model for questions, which
uses a biencoder to jointly perform mention detection and linking in one pass.
Evaluated on WebQSP and GraphQuestions with extended annotations that cover
multiple entities per question, ELQ outperforms the previous state of the art
by a large margin of +12.7% and +19.6% F1, respectively. With a very fast
inference time (1.57 examples/s on a single CPU), ELQ can be useful for
downstream question answering systems. In a proof-of-concept experiment, we
demonstrate that using ELQ significantly improves the downstream QA performance
of GraphRetriever (arXiv:1911.03868). Code and data available at
https://github.com/facebookresearch/BLINK/tree/master/elq
| 2,020 | Computation and Language |
Efficient Inference For Neural Machine Translation | Large Transformer models have achieved state-of-the-art results in neural
machine translation and have become standard in the field. In this work, we
look for the optimal combination of known techniques to optimize inference
speed without sacrificing translation quality. We conduct an empirical study
that stacks various approaches and demonstrates that combination of replacing
decoder self-attention with simplified recurrent units, adopting a deep encoder
and a shallow decoder architecture and multi-head attention pruning can achieve
up to 109% and 84% speedup on CPU and GPU respectively and reduce the number of
parameters by 25% while maintaining the same translation quality in terms of
BLEU.
| 2,020 | Computation and Language |
On the Role of Supervision in Unsupervised Constituency Parsing | We analyze several recent unsupervised constituency parsing models, which are
tuned with respect to the parsing $F_1$ score on the Wall Street Journal (WSJ)
development set (1,700 sentences). We introduce strong baselines for them, by
training an existing supervised parsing model (Kitaev and Klein, 2018) on the
same labeled examples they access. When training on the 1,700 examples, or even
when using only 50 examples for training and 5 for development, such a few-shot
parsing approach can outperform all the unsupervised parsing methods by a
significant margin. Few-shot parsing can be further improved by a simple data
augmentation method and self-training. This suggests that, in order to arrive
at fair conclusions, we should carefully consider the amount of labeled data
used for model development. We propose two protocols for future work on
unsupervised parsing: (i) use fully unsupervised criteria for hyperparameter
tuning and model selection; (ii) use as few labeled examples as possible for
model development, and compare to few-shot parsing trained on the same labeled
examples.
| 2,020 | Computation and Language |
UnQovering Stereotyping Biases via Underspecified Questions | While language embeddings have been shown to have stereotyping biases, how
these biases affect downstream question answering (QA) models remains
unexplored. We present UNQOVER, a general framework to probe and quantify
biases through underspecified questions. We show that a naive use of model
scores can lead to incorrect bias estimates due to two forms of reasoning
errors: positional dependence and question independence. We design a formalism
that isolates the aforementioned errors. As case studies, we use this metric to
analyze four important classes of stereotypes: gender, nationality, ethnicity,
and religion. We probe five transformer-based QA models trained on two QA
datasets, along with their underlying language models. Our broad study reveals
that (1) all these models, with and without fine-tuning, have notable
stereotyping biases in these classes; (2) larger models often have higher bias;
and (3) the effect of fine-tuning on bias varies strongly with the dataset and
the model size.
| 2,020 | Computation and Language |
Modeling Preconditions in Text with a Crowd-sourced Dataset | Preconditions provide a form of logical connection between events that
explains why some events occur together and information that is complementary
to the more widely studied relations such as causation, temporal ordering,
entailment, and discourse relations. Modeling preconditions in text has been
hampered in part due to the lack of large scale labeled data grounded in text.
This paper introduces PeKo, a crowd-sourced annotation of preconditions between
event pairs in newswire, an order of magnitude larger than prior text
annotations. To complement this new corpus, we also introduce two challenge
tasks aimed at modeling preconditions: (i) Precondition Identification -- a
standard classification task defined over pairs of event mentions, and (ii)
Precondition Generation -- a generative task aimed at testing a more general
ability to reason about a given event. Evaluation on both tasks shows that
modeling preconditions is challenging even for today's large language models
(LM). This suggests that precondition knowledge is not easily accessible in
LM-derived representations alone. Our generation results show that fine-tuning
an LM on PeKo yields better conditional relations than when trained on raw text
or temporally-ordered corpora.
| 2,020 | Computation and Language |
Multi-Fact Correction in Abstractive Text Summarization | Pre-trained neural abstractive summarization systems have dominated
extractive strategies on news summarization performance, at least in terms of
ROUGE. However, system-generated abstractive summaries often face the pitfall
of factual inconsistency: generating incorrect facts with respect to the source
text. To address this challenge, we propose Span-Fact, a suite of two factual
correction models that leverages knowledge learned from question answering
models to make corrections in system-generated summaries via span selection.
Our models employ single or multi-masking strategies to either iteratively or
auto-regressively replace entities in order to ensure semantic consistency
w.r.t. the source text, while retaining the syntactic structure of summaries
generated by abstractive summarization models. Experiments show that our models
significantly boost the factual consistency of system-generated summaries
without sacrificing summary quality in terms of both automatic metrics and
human evaluation.
| 2,020 | Computation and Language |
On the Branching Bias of Syntax Extracted from Pre-trained Language
Models | Many efforts have been devoted to extracting constituency trees from
pre-trained language models, often proceeding in two stages: feature definition
and parsing. However, this kind of methods may suffer from the branching bias
issue, which will inflate the performances on languages with the same branch it
biases to. In this work, we propose quantitatively measuring the branching bias
by comparing the performance gap on a language and its reversed language, which
is agnostic to both language models and extracting methods. Furthermore, we
analyze the impacts of three factors on the branching bias, namely parsing
algorithms, feature definitions, and language models. Experiments show that
several existing works exhibit branching biases, and some implementations of
these three factors can introduce the branching bias.
| 2,020 | Computation and Language |
Are Words Commensurate with Actions? Quantifying Commitment to a Cause
from Online Public Messaging | Public entities such as companies and politicians increasingly use online
social networks to communicate directly with their constituencies. Often, this
public messaging is aimed at aligning the entity with a particular cause or
issue, such as the environment or public health. However, as a consumer or
voter, it can be difficult to assess an entity's true commitment to a cause
based on public messaging. In this paper, we present a text classification
approach to categorize a message according to its commitment level toward a
cause. We then compare the volume of such messages with external ratings based
on entities' actions (e.g., a politician's voting record with respect to the
environment or a company's rating from environmental non-profits). We find that
by distinguishing between low- and high- level commitment messages, we can more
reliably identify truly committed entities. Furthermore, by measuring the
discrepancy between classified messages and external ratings, we can identify
entities whose public messaging does not align with their actions, thereby
providing a methodology to identify potentially "inauthentic" messaging
campaigns.
| 2,020 | Computation and Language |
Iterative Domain-Repaired Back-Translation | In this paper, we focus on the domain-specific translation with low
resources, where in-domain parallel corpora are scarce or nonexistent. One
common and effective strategy for this case is exploiting in-domain monolingual
data with the back-translation method. However, the synthetic parallel data is
very noisy because they are generated by imperfect out-of-domain systems,
resulting in the poor performance of domain adaptation. To address this issue,
we propose a novel iterative domain-repaired back-translation framework, which
introduces the Domain-Repair (DR) model to refine translations in synthetic
bilingual data. To this end, we construct corresponding data for the DR model
training by round-trip translating the monolingual sentences, and then design
the unified training framework to optimize paired DR and NMT models jointly.
Experiments on adapting NMT models between specific domains and from the
general domain to specific domains demonstrate the effectiveness of our
proposed approach, achieving 15.79 and 4.47 BLEU improvements on average over
unadapted models and back-translation.
| 2,020 | Computation and Language |
Pretrained Language Model Embryology: The Birth of ALBERT | While behaviors of pretrained language models (LMs) have been thoroughly
examined, what happened during pretraining is rarely studied. We thus
investigate the developmental process from a set of randomly initialized
parameters to a totipotent language model, which we refer to as the embryology
of a pretrained language model. Our results show that ALBERT learns to
reconstruct and predict tokens of different parts of speech (POS) in different
learning speeds during pretraining. We also find that linguistic knowledge and
world knowledge do not generally improve as pretraining proceeds, nor do
downstream tasks' performance. These findings suggest that knowledge of a
pretrained model varies during pretraining, and having more pretrain steps does
not necessarily provide a model with more comprehensive knowledge. We will
provide source codes and pretrained models to reproduce our results at
https://github.com/d223302/albert-embryology.
| 2,020 | Computation and Language |
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent
Detection | Few-shot Intent Detection is challenging due to the scarcity of available
annotated utterances. Although recent works demonstrate that multi-level
matching plays an important role in transferring learned knowledge from seen
training classes to novel testing classes, they rely on a static similarity
measure and overly fine-grained matching components. These limitations inhibit
generalizing capability towards Generalized Few-shot Learning settings where
both seen and novel classes are co-existent. In this paper, we propose a novel
Semantic Matching and Aggregation Network where semantic components are
distilled from utterances via multi-head self-attention with additional dynamic
regularization constraints. These semantic components capture high-level
information, resulting in more effective matching between instances. Our
multi-perspective matching method provides a comprehensive matching measure to
enhance representations of both labeled and unlabeled instances. We also
propose a more challenging evaluation setting that considers classification on
the joint all-class label space. Extensive experimental results demonstrate the
effectiveness of our method. Our code and data are publicly available.
| 2,020 | Computation and Language |
Help! Need Advice on Identifying Advice | Humans use language to accomplish a wide variety of tasks - asking for and
giving advice being one of them. In online advice forums, advice is mixed in
with non-advice, like emotional support, and is sometimes stated explicitly,
sometimes implicitly. Understanding the language of advice would equip systems
with a better grasp of language pragmatics; practically, the ability to
identify advice would drastically increase the efficiency of advice-seeking
online, as well as advice-giving in natural language generation systems.
We present a dataset in English from two Reddit advice forums - r/AskParents
and r/needadvice - annotated for whether sentences in posts contain advice or
not. Our analysis reveals rich linguistic phenomena in advice discourse. We
present preliminary models showing that while pre-trained language models are
able to capture advice better than rule-based systems, advice identification is
challenging, and we identify directions for future research.
Comments: To be presented at EMNLP 2020.
| 2,020 | Computation and Language |
Joint Turn and Dialogue level User Satisfaction Estimation on
Multi-Domain Conversations | Dialogue level quality estimation is vital for optimizing data driven
dialogue management. Current automated methods to estimate turn and dialogue
level user satisfaction employ hand-crafted features and rely on complex
annotation schemes, which reduce the generalizability of the trained models. We
propose a novel user satisfaction estimation approach which minimizes an
adaptive multi-task loss function in order to jointly predict turn-level
Response Quality labels provided by experts and explicit dialogue-level ratings
provided by end users. The proposed BiLSTM based deep neural net model
automatically weighs each turn's contribution towards the estimated
dialogue-level rating, implicitly encodes temporal dependencies, and removes
the need to hand-craft features.
On dialogues sampled from 28 Alexa domains, two dialogue systems and three
user groups, the joint dialogue-level satisfaction estimation model achieved up
to an absolute 27% (0.43->0.70) and 7% (0.63->0.70) improvement in linear
correlation performance over baseline deep neural net and benchmark Gradient
boosting regression models, respectively.
| 2,020 | Computation and Language |
GRUEN for Evaluating Linguistic Quality of Generated Text | Automatic evaluation metrics are indispensable for evaluating generated text.
To date, these metrics have focused almost exclusively on the content selection
aspect of the system output, ignoring the linguistic quality aspect altogether.
We bridge this gap by proposing GRUEN for evaluating Grammaticality,
non-Redundancy, focUs, structure and coherENce of generated text. GRUEN
utilizes a BERT-based model and a class of syntactic, semantic, and contextual
features to examine the system output. Unlike most existing evaluation metrics
which require human references as an input, GRUEN is reference-less and
requires only the system output. Besides, it has the advantage of being
unsupervised, deterministic, and adaptable to various tasks. Experiments on
seven datasets over four language generation tasks show that the proposed
metric correlates highly with human judgments.
| 2,020 | Computation and Language |
Efficient Meta Lifelong-Learning with Limited Memory | Current natural language processing models work well on a single task, yet
they often fail to continuously learn new tasks without forgetting previous
ones as they are re-trained throughout their lifetime, a challenge known as
lifelong learning. State-of-the-art lifelong language learning methods store
past examples in episodic memory and replay them at both training and inference
time. However, as we show later in our experiments, there are three significant
impediments: (1) needing unrealistically large memory module to achieve good
performance, (2) suffering from negative transfer, (3) requiring multiple local
adaptation steps for each test example that significantly slows down the
inference speed. In this paper, we identify three common principles of lifelong
learning methods and propose an efficient meta-lifelong framework that combines
them in a synergistic fashion. To achieve sample efficiency, our method trains
the model in a manner that it learns a better initialization for local
adaptation. Extensive experiments on text classification and question answering
benchmarks demonstrate the effectiveness of our framework by achieving
state-of-the-art performance using merely 1% memory size and narrowing the gap
with multi-task learning. We further show that our method alleviates both
catastrophic forgetting and negative transfer at the same time.
| 2,020 | Computation and Language |
Investigating African-American Vernacular English in Transformer-Based
Text Generation | The growth of social media has encouraged the written use of African American
Vernacular English (AAVE), which has traditionally been used only in oral
contexts. However, NLP models have historically been developed using dominant
English varieties, such as Standard American English (SAE), due to text corpora
availability. We investigate the performance of GPT-2 on AAVE text by creating
a dataset of intent-equivalent parallel AAVE/SAE tweet pairs, thereby isolating
syntactic structure and AAVE- or SAE-specific language for each pair. We
evaluate each sample and its GPT-2 generated text with pretrained sentiment
classifiers and find that while AAVE text results in more classifications of
negative sentiment than SAE, the use of GPT-2 generally increases occurrences
of positive sentiment for both. Additionally, we conduct human evaluation of
AAVE and SAE text generated with GPT-2 to compare contextual rigor and overall
quality.
| 2,020 | Computation and Language |
Multi-task Learning for Multilingual Neural Machine Translation | While monolingual data has been shown to be useful in improving bilingual
neural machine translation (NMT), effectively and efficiently leveraging
monolingual data for Multilingual NMT (MNMT) systems is a less explored area.
In this work, we propose a multi-task learning (MTL) framework that jointly
trains the model with the translation task on bitext data and two denoising
tasks on the monolingual data. We conduct extensive empirical studies on MNMT
systems with 10 language pairs from WMT datasets. We show that the proposed
approach can effectively improve the translation quality for both high-resource
and low-resource languages with large margin, achieving significantly better
results than the individual bilingual models. We also demonstrate the efficacy
of the proposed approach in the zero-shot setup for language pairs without
bitext training data. Furthermore, we show the effectiveness of MTL over
pre-training approaches for both NMT and cross-lingual transfer learning NLU
tasks; the proposed approach outperforms massive scale models trained on single
task.
| 2,020 | Computation and Language |
An Empirical Study of Tokenization Strategies for Various Korean NLP
Tasks | Typically, tokenization is the very first step in most text processing works.
As a token serves as an atomic unit that embeds the contextual information of
text, how to define a token plays a decisive role in the performance of a
model.Even though Byte Pair Encoding (BPE) has been considered the de facto
standard tokenization method due to its simplicity and universality, it still
remains unclear whether BPE works best across all languages and tasks. In this
paper, we test several tokenization strategies in order to answer our primary
research question, that is, "What is the best tokenization strategy for Korean
NLP tasks?" Experimental results demonstrate that a hybrid approach of
morphological segmentation followed by BPE works best in Korean to/from English
machine translation and natural language understanding tasks such as KorNLI,
KorSTS, NSMC, and PAWS-X. As an exception, for KorQuAD, the Korean extension of
SQuAD, BPE segmentation turns out to be the most effective.
| 2,020 | Computation and Language |
Do Explicit Alignments Robustly Improve Multilingual Encoders? | Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised
multilingual encoders can effectively learn cross-lingual representation.
Explicit alignment objectives based on bitexts like Europarl or MultiUN have
been shown to further improve these representations. However, word-level
alignments are often suboptimal and such bitexts are unavailable for many
languages. In this paper, we propose a new contrastive alignment objective that
can better utilize such signal, and examine whether these previous alignment
methods can be adapted to noisier sources of aligned data: a randomly sampled 1
million pair subset of the OPUS collection. Additionally, rather than report
results on a single dataset with a single model run, we report the mean and
standard derivation of multiple runs with different seeds, on four datasets and
tasks. Our more extensive analysis finds that, while our new objective
outperforms previous work, overall these methods do not improve performance
with a more robust evaluation framework. Furthermore, the gains from using a
better underlying model eclipse any benefits from alignment training. These
negative results dictate more care in evaluating these methods and suggest
limitations in applying explicit alignment objectives.
| 2,020 | Computation and Language |
Please Mind the Root: Decoding Arborescences for Dependency Parsing | The connection between dependency trees and spanning trees is exploited by
the NLP community to train and to decode graph-based dependency parsers.
However, the NLP literature has missed an important difference between the two
structures: only one edge may emanate from the root in a dependency tree. We
analyzed the output of state-of-the-art parsers on many languages from the
Universal Dependency Treebank: although these parsers are often able to learn
that trees which violate the constraint should be assigned lower probabilities,
their ability to do so unsurprisingly de-grades as the size of the training set
decreases. In fact, the worst constraint-violation rate we observe is 24%.
Prior work has proposed an inefficient algorithm to enforce the constraint,
which adds a factor of n to the decoding runtime. We adapt an algorithm due to
Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint
without compromising the original runtime.
| 2,020 | Computation and Language |
Data Rejuvenation: Exploiting Inactive Training Examples for Neural
Machine Translation | Large-scale training datasets lie at the core of the recent success of neural
machine translation (NMT) models. However, the complex patterns and potential
noises in the large-scale data make training NMT models difficult. In this
work, we explore to identify the inactive training examples which contribute
less to the model performance, and show that the existence of inactive examples
depends on the data distribution. We further introduce data rejuvenation to
improve the training of NMT models on large-scale datasets by exploiting
inactive examples. The proposed framework consists of three phases. First, we
train an identification model on the original training data, and use it to
distinguish inactive examples and active examples by their sentence-level
output probabilities. Then, we train a rejuvenation model on the active
examples, which is used to re-label the inactive examples with
forward-translation. Finally, the rejuvenated examples and the active examples
are combined to train the final NMT model. Experimental results on WMT14
English-German and English-French datasets show that the proposed data
rejuvenation consistently and significantly improves performance for several
strong NMT models. Extensive analyses reveal that our approach stabilizes and
accelerates the training process of NMT models, resulting in final models with
better generalization capability.
| 2,020 | Computation and Language |
PolicyQA: A Reading Comprehension Dataset for Privacy Policies | Privacy policy documents are long and verbose. A question answering (QA)
system can assist users in finding the information that is relevant and
important to them. Prior studies in this domain frame the QA task as retrieving
the most relevant text segment or a list of sentences from the policy document
given a question. On the contrary, we argue that providing users with a short
text span from policy documents reduces the burden of searching the target
information from a lengthy text segment. In this paper, we present PolicyQA, a
dataset that contains 25,017 reading comprehension style examples curated from
an existing corpus of 115 website privacy policies. PolicyQA provides 714
human-annotated questions written for a wide range of privacy practices. We
evaluate two existing neural QA models and perform rigorous analysis to reveal
the advantages and challenges offered by PolicyQA.
| 2,020 | Computation and Language |
LEGAL-BERT: The Muppets straight out of Law School | BERT has achieved impressive performance in several NLP tasks. However, there
has been limited investigation on its adaptation guidelines in specialised
domains. Here we focus on the legal domain, where we explore several approaches
for applying BERT models to downstream legal tasks, evaluating on multiple
datasets. Our findings indicate that the previous guidelines for pre-training
and fine-tuning, often blindly followed, do not always generalize well in the
legal domain. Thus we propose a systematic investigation of the available
strategies when applying BERT in specialised domains. These are: (a) use the
original BERT out of the box, (b) adapt BERT by additional pre-training on
domain-specific corpora, and (c) pre-train BERT from scratch on domain-specific
corpora. We also propose a broader hyper-parameter search space when
fine-tuning for downstream tasks and we release LEGAL-BERT, a family of BERT
models intended to assist legal NLP research, computational law, and legal
technology applications.
| 2,020 | Computation and Language |
Cross-Lingual Text Classification with Minimal Resources by Transferring
a Sparse Teacher | Cross-lingual text classification alleviates the need for manually labeled
documents in a target language by leveraging labeled documents from other
languages. Existing approaches for transferring supervision across languages
require expensive cross-lingual resources, such as parallel corpora, while less
expensive cross-lingual representation learning approaches train classifiers
without target labeled documents. In this work, we propose a cross-lingual
teacher-student method, CLTS, that generates "weak" supervision in the target
language using minimal cross-lingual resources, in the form of a small number
of word translations. Given a limited translation budget, CLTS extracts and
transfers only the most important task-specific seed words across languages and
initializes a teacher classifier based on the translated seed words. Then, CLTS
iteratively trains a more powerful student that also exploits the context of
the seed words in unlabeled target documents and outperforms the teacher. CLTS
is simple and surprisingly effective in 18 diverse languages: by transferring
just 20 seed words, even a bag-of-words logistic regression student outperforms
state-of-the-art cross-lingual methods (e.g., based on multilingual BERT).
Moreover, CLTS can accommodate any type of student classifier: leveraging a
monolingual BERT student leads to further improvements and outperforms even
more expensive approaches by up to 12% in accuracy. Finally, CLTS addresses
emerging tasks in low-resource languages using just a small number of word
translations.
| 2,020 | Computation and Language |
SupMMD: A Sentence Importance Model for Extractive Summarization using
Maximum Mean Discrepancy | Most work on multi-document summarization has focused on generic
summarization of information present in each individual document set. However,
the under-explored setting of update summarization, where the goal is to
identify the new information present in each set, is of equal practical
interest (e.g., presenting readers with updates on an evolving news topic). In
this work, we present SupMMD, a novel technique for generic and update
summarization based on the maximum mean discrepancy from kernel two-sample
testing. SupMMD combines both supervised learning for salience and unsupervised
learning for coverage and diversity. Further, we adapt multiple kernel learning
to make use of similarity across multiple information sources (e.g., text
features and knowledge based concepts). We show the efficacy of SupMMD in both
generic and update summarization tasks by meeting or exceeding the current
state-of-the-art on the DUC-2004 and TAC-2009 datasets.
| 2,020 | Computation and Language |
StyleDGPT: Stylized Response Generation with Pre-trained Language Models | Generating responses following a desired style has great potentials to extend
applications of open-domain dialogue systems, yet is refrained by lacking of
parallel data for training. In this work, we explore the challenging task with
pre-trained language models that have brought breakthrough to various natural
language tasks. To this end, we introduce a KL loss and a style classifier to
the fine-tuning step in order to steer response generation towards the target
style in both a word-level and a sentence-level. Comprehensive empirical
studies with two public datasets indicate that our model can significantly
outperform state-of-the-art methods in terms of both style consistency and
contextual coherence.
| 2,020 | Computation and Language |
Does the Objective Matter? Comparing Training Objectives for Pronoun
Resolution | Hard cases of pronoun resolution have been used as a long-standing benchmark
for commonsense reasoning. In the recent literature, pre-trained language
models have been used to obtain state-of-the-art results on pronoun resolution.
Overall, four categories of training and evaluation objectives have been
introduced. The variety of training datasets and pre-trained language models
used in these works makes it unclear whether the choice of training objective
is critical. In this work, we make a fair comparison of the performance and
seed-wise stability of four models that represent the four categories of
objectives. Our experiments show that the objective of sequence ranking
performs the best in-domain, while the objective of semantic similarity between
candidates and pronoun performs the best out-of-domain. We also observe a
seed-wise instability of the model using sequence ranking, which is not the
case when the other objectives are used.
| 2,020 | Computation and Language |
The Multilingual Amazon Reviews Corpus | We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale
collection of Amazon reviews for multilingual text classification. The corpus
contains reviews in English, Japanese, German, French, Spanish, and Chinese,
which were collected between 2015 and 2019. Each record in the dataset contains
the review text, the review title, the star rating, an anonymized reviewer ID,
an anonymized product ID, and the coarse-grained product category (e.g.,
'books', 'appliances', etc.) The corpus is balanced across the 5 possible star
ratings, so each rating constitutes 20% of the reviews in each language. For
each language, there are 200,000, 5,000, and 5,000 reviews in the training,
development, and test sets, respectively. We report baseline results for
supervised text classification and zero-shot cross-lingual transfer learning by
fine-tuning a multilingual BERT model on reviews data. We propose the use of
mean absolute error (MAE) instead of classification accuracy for this task,
since MAE accounts for the ordinal nature of the ratings.
| 2,020 | Computation and Language |
Universal Natural Language Processing with Limited Annotations: Try
Few-shot Textual Entailment as a Start | A standard way to address different NLP problems is by first constructing a
problem-specific dataset, then building a model to fit this dataset. To build
the ultimate artificial intelligence, we desire a single machine that can
handle diverse new problems, for which task-specific annotations are limited.
We bring up textual entailment as a unified solver for such NLP problems.
However, current research of textual entailment has not spilled much ink on the
following questions: (i) How well does a pretrained textual entailment system
generalize across domains with only a handful of domain-specific examples? and
(ii) When is it worth transforming an NLP task into textual entailment? We
argue that the transforming is unnecessary if we can obtain rich annotations
for this task. Textual entailment really matters particularly when the target
NLP task has insufficient annotations.
Universal NLP can be probably achieved through different routines. In this
work, we introduce Universal Few-shot textual Entailment (UFO-Entail). We
demonstrate that this framework enables a pretrained entailment model to work
well on new entailment domains in a few-shot setting, and show its
effectiveness as a unified solver for several downstream NLP tasks such as
question answering and coreference resolution when the end-task annotations are
limited. Code: https://github.com/salesforce/UniversalFewShotNLP
| 2,020 | Computation and Language |
Knowing What You Know: Calibrating Dialogue Belief State Distributions
via Ensembles | The ability to accurately track what happens during a conversation is
essential for the performance of a dialogue system. Current state-of-the-art
multi-domain dialogue state trackers achieve just over 55% accuracy on the
current go-to benchmark, which means that in almost every second dialogue turn
they place full confidence in an incorrect dialogue state. Belief trackers, on
the other hand, maintain a distribution over possible dialogue states. However,
they lack in performance compared to dialogue state trackers, and do not
produce well calibrated distributions. In this work we present state-of-the-art
performance in calibration for multi-domain dialogue belief trackers using a
calibrated ensemble of models. Our resulting dialogue belief tracker also
outperforms previous dialogue belief tracking models in terms of accuracy.
| 2,020 | Computation and Language |
Dissecting Span Identification Tasks with Performance Prediction | Span identification (in short, span ID) tasks such as chunking, NER, or
code-switching detection, ask models to identify and classify relevant spans in
a text. Despite being a staple of NLP, and sharing a common structure, there is
little insight on how these tasks' properties influence their difficulty, and
thus little guidance on what model families work well on span ID tasks, and
why. We analyze span ID tasks via performance prediction, estimating how well
neural architectures do on different tasks. Our contributions are: (a) we
identify key properties of span ID tasks that can inform performance
prediction; (b) we carry out a large-scale experiment on English data, building
a model to predict performance for unseen span ID tasks that can support
architecture choices; (c), we investigate the parameters of the meta model,
yielding new insights on how model and task properties interact to affect span
ID performance. We find, e.g., that span frequency is especially important for
LSTMs, and that CRFs help when spans are infrequent and boundaries
non-distinctive.
| 2,020 | Computation and Language |
CoRefi: A Crowd Sourcing Suite for Coreference Annotation | Coreference annotation is an important, yet expensive and time consuming,
task, which often involved expert annotators trained on complex decision
guidelines. To enable cheaper and more efficient annotation, we present CoRefi,
a web-based coreference annotation suite, oriented for crowdsourcing. Beyond
the core coreference annotation tool, CoRefi provides guided onboarding for the
task as well as a novel algorithm for a reviewing phase. CoRefi is open source
and directly embeds into any website, including popular crowdsourcing
platforms.
CoRefi Demo: aka.ms/corefi Video Tour: aka.ms/corefivideo Github Repo:
https://github.com/aribornstein/corefi
| 2,020 | Computation and Language |
Scene Graph Modification Based on Natural Language Commands | Structured representations like graphs and parse trees play a crucial role in
many Natural Language Processing systems. In recent years, the advancements in
multi-turn user interfaces necessitate the need for controlling and updating
these structured representations given new sources of information. Although
there have been many efforts focusing on improving the performance of the
parsers that map text to graphs or parse trees, very few have explored the
problem of directly manipulating these representations. In this paper, we
explore the novel problem of graph modification, where the systems need to
learn how to update an existing scene graph given a new user's command. Our
novel models based on graph-based sparse transformer and cross attention
information fusion outperform previous systems adapted from the machine
translation and graph generation literature. We further contribute our large
graph modification datasets to the research community to encourage future
research for this new problem.
| 2,020 | Computation and Language |
Semantically Driven Sentence Fusion: Modeling and Evaluation | Sentence fusion is the task of joining related sentences into coherent text.
Current training and evaluation schemes for this task are based on single
reference ground-truths and do not account for valid fusion variants. We show
that this hinders models from robustly capturing the semantic relationship
between input sentences. To alleviate this, we present an approach in which
ground-truth solutions are automatically expanded into multiple references via
curated equivalence classes of connective phrases. We apply this method to a
large-scale dataset and use the augmented dataset for both model training and
evaluation. To improve the learning of semantic representation using multiple
references, we enrich the model with auxiliary discourse classification tasks
under a multi-tasking framework. Our experiments highlight the improvements of
our approach over state-of-the-art models.
| 2,020 | Computation and Language |
Embedding Words in Non-Vector Space with Unsupervised Graph Learning | It has become a de-facto standard to represent words as elements of a vector
space (word2vec, GloVe). While this approach is convenient, it is unnatural for
language: words form a graph with a latent hierarchical structure, and this
structure has to be revealed and encoded by word embeddings. We introduce
GraphGlove: unsupervised graph word representations which are learned
end-to-end. In our setting, each word is a node in a weighted graph and the
distance between words is the shortest path distance between the corresponding
nodes. We adopt a recent method learning a representation of data in the form
of a differentiable weighted graph and use it to modify the GloVe training
algorithm. We show that our graph-based representations substantially
outperform vector-based methods on word similarity and analogy tasks. Our
analysis reveals that the structure of the learned graphs is hierarchical and
similar to that of WordNet, the geometry is highly non-trivial and contains
subgraphs with different local topology.
| 2,020 | Computation and Language |
Converting the Point of View of Messages Spoken to Virtual Assistants | Virtual Assistants can be quite literal at times. If the user says "tell Bob
I love him," most virtual assistants will extract the message "I love him" and
send it to the user's contact named Bob, rather than properly converting the
message to "I love you." We designed a system to allow virtual assistants to
take a voice message from one user, convert the point of view of the message,
and then deliver the result to its target user. We developed a rule-based
model, which integrates a linear text classification model, part-of-speech
tagging, and constituency parsing with rule-based transformation methods. We
also investigated Neural Machine Translation (NMT) approaches, including LSTMs,
CopyNet, and T5. We explored 5 metrics to gauge both naturalness and
faithfulness automatically, and we chose to use BLEU plus METEOR for
faithfulness and relative perplexity using a separately trained language model
(GPT) for naturalness. Transformer-Copynet and T5 performed similarly on
faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a
METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity
of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly
released our dataset, which is composed of 46,565 crowd-sourced samples.
| 2,020 | Computation and Language |
DaNetQA: a yes/no Question Answering Dataset for the Russian Language | DaNetQA, a new question-answering corpus, follows (Clark et. al, 2019)
design: it comprises natural yes/no questions. Each question is paired with a
paragraph from Wikipedia and an answer, derived from the paragraph. The task is
to take both the question and a paragraph as input and come up with a yes/no
answer, i.e. to produce a binary output. In this paper, we present a
reproducible approach to DaNetQA creation and investigate transfer learning
methods for task and language transferring. For task transferring we leverage
three similar sentence modelling tasks: 1) a corpus of paraphrases,
Paraphraser, 2) an NLI task, for which we use the Russian part of XNLI, 3)
another question answering task, SberQUAD. For language transferring we use
English to Russian translation together with multilingual language fine-tuning.
| 2,023 | Computation and Language |
Position-Aware Tagging for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the
triplets of target entities, their associated sentiment, and opinion spans
explaining the reason for the sentiment. Existing research efforts mostly solve
this problem using pipeline approaches, which break the triplet extraction
process into several stages. Our observation is that the three elements within
a triplet are highly related to each other, and this motivates us to build a
joint model to extract such triplets using a sequence tagging approach.
However, how to effectively design a tagging approach to extract the triplets
that can capture the rich interactions among the elements is a challenging
research question. In this work, we propose the first end-to-end model with a
novel position-aware tagging scheme that is capable of jointly extracting the
triplets. Our experimental results on several existing datasets show that
jointly capturing elements in the triplet using our approach leads to improved
performance over the existing approaches. We also conducted extensive
experiments to investigate the model effectiveness and robustness.
| 2,021 | Computation and Language |
On the Interplay Between Fine-tuning and Sentence-level Probing for
Linguistic Knowledge in Pre-trained Transformers | Fine-tuning pre-trained contextualized embedding models has become an
integral part of the NLP pipeline. At the same time, probing has emerged as a
way to investigate the linguistic knowledge captured by pre-trained models.
Very little is, however, understood about how fine-tuning affects the
representations of pre-trained models and thereby the linguistic knowledge they
encode. This paper contributes towards closing this gap. We study three
different pre-trained models: BERT, RoBERTa, and ALBERT, and investigate
through sentence-level probing how fine-tuning affects their representations.
We find that for some probing tasks fine-tuning leads to substantial changes in
accuracy, possibly suggesting that fine-tuning introduces or even removes
linguistic knowledge from a pre-trained model. These changes, however, vary
greatly across different models, fine-tuning and probing tasks. Our analysis
reveals that while fine-tuning indeed changes the representations of a
pre-trained model and these changes are typically larger for higher layers,
only in very few cases, fine-tuning has a positive effect on probing accuracy
that is larger than just using the pre-trained model with a strong pooling
method. Based on our findings, we argue that both positive and negative effects
of fine-tuning on probing require a careful interpretation.
| 2,020 | Computation and Language |
Neural Speech Synthesis for Estonian | This technical report describes the results of a collaboration between the
NLP research group at the University of Tartu and the Institute of Estonian
Language on improving neural speech synthesis for Estonian. The report (written
in Estonian) describes the project results, the summary of which is: (1) Speech
synthesis data from 6 speakers for a total of 92.4 hours is collected and
openly released (CC-BY-4.0). Data available at https://konekorpus.tartunlp.ai
and https://www.eki.ee/litsents/. (2) software and models for neural speech
synthesis is released open-source (MIT license). Available at
https://koodivaramu.eesti.ee/tartunlp/text-to-speech . (3) We ran evaluations
of the new models and compared them to other existing solutions (HMM-based HTS
models from EKI, http://www.eki.ee/heli/, and Google's speech synthesis for
Estonian, accessed via https://translate.google.com). Evaluation includes voice
acceptability MOS scores for sentence-level and longer excerpts, detailed error
analysis and evaluation of the pre-processing module.
| 2,020 | Computation and Language |
On the Sparsity of Neural Machine Translation Models | Modern neural machine translation (NMT) models employ a large number of
parameters, which leads to serious over-parameterization and typically causes
the underutilization of computational resources. In response to this problem,
we empirically investigate whether the redundant parameters can be reused to
achieve better performance. Experiments and analyses are systematically
conducted on different datasets and NMT architectures. We show that: 1) the
pruned parameters can be rejuvenated to improve the baseline model by up to
+0.8 BLEU points; 2) the rejuvenated parameters are reallocated to enhance the
ability of modeling low-level lexical information.
| 2,020 | Computation and Language |
On the Sub-Layer Functionalities of Transformer Decoder | There have been significant efforts to interpret the encoder of
Transformer-based encoder-decoder architectures for neural machine translation
(NMT); meanwhile, the decoder remains largely unexamined despite its critical
role. During translation, the decoder must predict output tokens by considering
both the source-language text from the encoder and the target-language prefix
produced in previous steps. In this work, we study how Transformer-based
decoders leverage information from the source and target languages --
developing a universal probe task to assess how information is propagated
through each module of each decoder layer. We perform extensive experiments on
three major translation datasets (WMT En-De, En-Fr, and En-Zh). Our analysis
provides insight on when and where decoders leverage different sources. Based
on these insights, we demonstrate that the residual feed-forward module in each
Transformer decoder layer can be dropped with minimal loss of performance -- a
significant reduction in computation and number of parameters, and consequently
a significant boost to both training and inference speed.
| 2,020 | Computation and Language |
Context Modeling with Evidence Filter for Multiple Choice Question
Answering | Multiple-Choice Question Answering (MCQA) is a challenging task in machine
reading comprehension. The main challenge in MCQA is to extract "evidence" from
the given context that supports the correct answer. In the OpenbookQA dataset,
the requirement of extracting "evidence" is particularly important due to the
mutual independence of sentences in the context. Existing work tackles this
problem by annotated evidence or distant supervision with rules which overly
rely on human efforts. To address the challenge, we propose a simple yet
effective approach termed evidence filtering to model the relationships between
the encoded contexts with respect to different options collectively and to
potentially highlight the evidence sentences and filter out unrelated
sentences. In addition to the effective reduction of human efforts of our
approach compared, through extensive experiments on OpenbookQA, we show that
the proposed approach outperforms the models that use the same backbone and
more training data; and our parameter analysis also demonstrates the
interpretability of our approach.
| 2,020 | Computation and Language |
If beam search is the answer, what was the question? | Quite surprisingly, exact maximum a posteriori (MAP) decoding of neural
language generators frequently leads to low-quality results. Rather, most
state-of-the-art results on language generation tasks are attained using beam
search despite its overwhelmingly high search error rate. This implies that the
MAP objective alone does not express the properties we desire in text, which
merits the question: if beam search is the answer, what was the question? We
frame beam search as the exact solution to a different decoding objective in
order to gain insights into why high probability under a model alone may not
indicate adequacy. We find that beam search enforces uniform information
density in text, a property motivated by cognitive science. We suggest a set of
decoding objectives that explicitly enforce this property and find that exact
decoding with these objectives alleviates the problems encountered when
decoding poorly calibrated language generation models. Additionally, we analyze
the text produced using various decoding strategies and see that, in our neural
machine translation experiments, the extent to which this property is adhered
to strongly correlates with BLEU.
| 2,021 | Computation and Language |
Extracting Implicitly Asserted Propositions in Argumentation | Argumentation accommodates various rhetorical devices, such as questions,
reported speech, and imperatives. These rhetorical tools usually assert
argumentatively relevant propositions rather implicitly, so understanding their
true meaning is key to understanding certain arguments properly. However, most
argument mining systems and computational linguistics research have paid little
attention to implicitly asserted propositions in argumentation. In this paper,
we examine a wide range of computational methods for extracting propositions
that are implicitly asserted in questions, reported speech, and imperatives in
argumentation. By evaluating the models on a corpus of 2016 U.S. presidential
debates and online commentary, we demonstrate the effectiveness and limitations
of the computational models. Our study may inform future research on argument
mining and the semantics of these rhetorical devices in argumentation.
| 2,020 | Computation and Language |
Multi-Instance Multi-Label Learning Networks for Aspect-Category
Sentiment Analysis | Aspect-category sentiment analysis (ACSA) aims to predict sentiment
polarities of sentences with respect to given aspect categories. To detect the
sentiment toward a particular aspect category in a sentence, most previous
methods first generate an aspect category-specific sentence representation for
the aspect category, then predict the sentiment polarity based on the
representation. These methods ignore the fact that the sentiment of an aspect
category mentioned in a sentence is an aggregation of the sentiments of the
words indicating the aspect category in the sentence, which leads to suboptimal
performance. In this paper, we propose a Multi-Instance Multi-Label Learning
Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats
sentences as bags, words as instances, and the words indicating an aspect
category as the key instances of the aspect category. Given a sentence and the
aspect categories mentioned in the sentence, AC-MIMLLN first predicts the
sentiments of the instances, then finds the key instances for the aspect
categories, finally obtains the sentiments of the sentence toward the aspect
categories by aggregating the key instance sentiments. Experimental results on
three public datasets demonstrate the effectiveness of AC-MIMLLN.
| 2,020 | Computation and Language |
Detecting Attackable Sentences in Arguments | Finding attackable sentences in an argument is the first step toward
successful refutation in argumentation. We present a first large-scale analysis
of sentence attackability in online arguments. We analyze driving reasons for
attacks in argumentation and identify relevant characteristics of sentences. We
demonstrate that a sentence's attackability is associated with many of these
characteristics regarding the sentence's content, proposition types, and tone,
and that an external knowledge source can provide useful information about
attackability. Building on these findings, we demonstrate that machine learning
models can automatically detect attackable sentences in arguments,
significantly better than several baselines and comparably well to laypeople.
| 2,020 | Computation and Language |
Metaphor Interpretation Using Word Embeddings | We suggest a model for metaphor interpretation using word embeddings trained
over a relatively large corpus. Our system handles nominal metaphors, like
"time is money". It generates a ranked list of potential interpretations of
given metaphors. Candidate meanings are drawn from collocations of the topic
("time") and vehicle ("money") components, automatically extracted from a
dependency-parsed corpus. We explore adding candidates derived from word
association norms (common human responses to cues). Our ranking procedure
considers similarity between candidate interpretations and metaphor components,
measured in a semantic vector space. Lastly, a clustering algorithm removes
semantically related duplicates, thereby allowing other candidate
interpretations to attain higher rank. We evaluate using different sets of
annotated metaphors, with encouraging preliminary results.
| 2,021 | Computation and Language |
Incorporating Behavioral Hypotheses for Query Generation | Generative neural networks have been shown effective on query suggestion.
Commonly posed as a conditional generation problem, the task aims to leverage
earlier inputs from users in a search session to predict queries that they will
likely issue at a later time. User inputs come in various forms such as
querying and clicking, each of which can imply different semantic signals
channeled through the corresponding behavioral patterns. This paper induces
these behavioral biases as hypotheses for query generation, where a generic
encoder-decoder Transformer framework is presented to aggregate arbitrary
hypotheses of choice. Our experimental results show that the proposed approach
leads to significant improvements on top-$k$ word error rate and Bert F1 Score
compared to a recent BART model.
| 2,020 | Computation and Language |
Poison Attacks against Text Datasets with Conditional Adversarially
Regularized Autoencoder | This paper demonstrates a fatal vulnerability in natural language inference
(NLI) and text classification systems. More concretely, we present a 'backdoor
poisoning' attack on NLP models. Our poisoning attack utilizes conditional
adversarially regularized autoencoder (CARA) to generate poisoned training
samples by poison injection in latent space. Just by adding 1% poisoned data,
our experiments show that a victim BERT finetuned classifier's predictions can
be steered to the poison target class with success rates of >80% when the input
hypothesis is injected with the poison signature, demonstrating that NLI and
text classification systems face a huge security risk.
| 2,020 | Computation and Language |
BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking
Scalar Adjectives with Contextualised Representations | Adjectives like pretty, beautiful and gorgeous describe positive properties
of the nouns they modify but with different intensity. These differences are
important for natural language understanding and reasoning. We propose a novel
BERT-based approach to intensity detection for scalar adjectives. We model
intensity by vectors directly derived from contextualised representations and
show they can successfully rank scalar adjectives. We evaluate our models both
intrinsically, on gold standard datasets, and on an Indirect Question Answering
task. Our results demonstrate that BERT encodes rich knowledge about the
semantics of scalar adjectives, and is able to provide better quality intensity
rankings than static embeddings and previous models with access to dedicated
resources.
| 2,020 | Computation and Language |
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection
and Slot Filling | Slot filling and intent detection are two main tasks in spoken language
understanding (SLU) system. In this paper, we propose a novel
non-autoregressive model named SlotRefine for joint intent detection and slot
filling. Besides, we design a novel two-pass iteration mechanism to handle the
uncoordinated slots problem caused by conditional independence of
non-autoregressive model. Experiments demonstrate that our model significantly
outperforms previous models in slot filling task, while considerably speeding
up the decoding (up to X 10.77). In-depth analyses show that 1) pretraining
schemes could further enhance our model; 2) two-pass mechanism indeed remedy
the uncoordinated slots.
| 2,020 | Computation and Language |
Analyzing Individual Neurons in Pre-trained Language Models | While a lot of analysis has been carried to demonstrate linguistic knowledge
captured by the representations learned within deep NLP models, very little
attention has been paid towards individual neurons.We carry outa neuron-level
analysis using core linguistic tasks of predicting morphology, syntax and
semantics, on pre-trained language models, with questions like: i) do
individual neurons in pre-trained models capture linguistic information? ii)
which parts of the network learn more about certain linguistic phenomena? iii)
how distributed or focused is the information? and iv) how do various
architectures differ in learning these properties? We found small subsets of
neurons to predict linguistic tasks, with lower level tasks (such as
morphology) localized in fewer neurons, compared to higher level task of
predicting syntax. Our study also reveals interesting cross architectural
comparisons. For example, we found neurons in XLNet to be more localized and
disjoint when predicting properties compared to BERT and others, where they are
more distributed and coupled.
| 2,020 | Computation and Language |
Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans | Aspect based sentiment analysis, predicting sentiment polarity of given
aspects, has drawn extensive attention. Previous attention-based models
emphasize using aspect semantics to help extract opinion features for
classification. However, these works are either not able to capture opinion
spans as a whole, or not able to capture variable-length opinion spans. In this
paper, we present a neat and effective structured attention model by
aggregating multiple linear-chain CRFs. Such a design allows the model to
extract aspect-specific opinion spans and then evaluate sentiment polarity by
exploiting the extracted opinion features. The experimental results on four
datasets demonstrate the effectiveness of the proposed model, and our analysis
demonstrates that our model can capture aspect-specific opinion spans.
| 2,021 | Computation and Language |
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for
Language Model Adaptation | We propose a method to automatically generate a domain- and task-adaptive
maskings of the given text for self-supervised pre-training, such that we can
effectively adapt the language model to a particular target task (e.g. question
answering). Specifically, we present a novel reinforcement learning-based
framework which learns the masking policy, such that using the generated masks
for further pre-training of the target language model helps improve task
performance on unseen texts. We use off-policy actor-critic with entropy
regularization and experience replay for reinforcement learning, and propose a
Transformer-based policy network that can consider the relative importance of
words in a given text. We validate our Neural Mask Generator (NMG) on several
question answering and text classification datasets using BERT and DistilBERT
as the language models, on which it outperforms rule-based masking strategies,
by automatically learning optimal adaptive maskings.
| 2,020 | Computation and Language |
Notes on Coalgebras in Stylometry | The syntactic behaviour of texts can highly vary depending on their contexts
(e.g. author, genre, etc.). From the standpoint of stylometry, it can be
helpful to objectively measure this behaviour. In this paper, we discuss how
coalgebras are used to formalise the notion of behaviour by embedding syntactic
features of a given text into probabilistic transition systems. By introducing
the behavioural distance, we are then able to quantitatively measure
differences between points in these systems and thus, comparing features of
different texts. Furthermore, the behavioural distance of points can be
approximated by a polynomial-time algorithm.
| 2,021 | Computation and Language |
Stepwise Extractive Summarization and Planning with Structured
Transformers | We propose encoder-centric stepwise models for extractive summarization using
structured transformers -- HiBERT and Extended Transformers. We enable stepwise
summarization by injecting the previously generated summary into the structured
transformer as an auxiliary sub-structure. Our models are not only efficient in
modeling the structure of long inputs, but they also do not rely on
task-specific redundancy-aware modeling, making them a general purpose
extractive content planner for different tasks. When evaluated on CNN/DailyMail
extractive summarization, stepwise models achieve state-of-the-art performance
in terms of Rouge without any redundancy aware modeling or sentence filtering.
This also holds true for Rotowire table-to-text generation, where our models
surpass previously reported metrics for content selection, planning and
ordering, highlighting the strength of stepwise modeling. Amongst the two
structured transformers we test, stepwise Extended Transformers provides the
best performance across both datasets and sets a new standard for these
challenges.
| 2,020 | Computation and Language |
A Multi-Task Incremental Learning Framework with Category Name Embedding
for Aspect-Category Sentiment Analysis | (T)ACSA tasks, including aspect-category sentiment analysis (ACSA) and
targeted aspect-category sentiment analysis (TACSA), aims at identifying
sentiment polarity on predefined categories. Incremental learning on new
categories is necessary for (T)ACSA real applications. Though current
multi-task learning models achieve good performance in (T)ACSA tasks, they
suffer from catastrophic forgetting problems in (T)ACSA incremental learning
tasks. In this paper, to make multi-task learning feasible for incremental
learning, we proposed Category Name Embedding network (CNE-net). We set both
encoder and decoder shared among all categories to weaken the catastrophic
forgetting problem. Besides the origin input sentence, we applied another input
feature, i.e., category name, for task discrimination. Our model achieved
state-of-the-art on two (T)ACSA benchmark datasets. Furthermore, we proposed a
dataset for (T)ACSA incremental learning and achieved the best performance
compared with other strong baselines.
| 2,020 | Computation and Language |
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based
Inference Networks | Many tasks in natural language processing involve predicting structured
outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine
translation. Researchers are increasingly applying deep representation learning
to these problems, but the structured component of these approaches is usually
quite simplistic. In this work, we propose several high-order energy terms to
capture complex dependencies among labels in sequence labeling, including
several that consider the entire label sequence. We use neural
parameterizations for these energy terms, drawing from convolutional,
recurrent, and self-attention networks. We use the framework of learning
energy-based inference networks (Tu and Gimpel, 2018) for dealing with the
difficulties of training and inference with such models. We empirically
demonstrate that this approach achieves substantial improvement using a variety
of high-order energy terms on four sequence labeling tasks, while having the
same decoding speed as simple, local classifiers. We also find high-order
energies to help in noisy data conditions.
| 2,020 | Computation and Language |
COSMIC: COmmonSense knowledge for eMotion Identification in
Conversations | In this paper, we address the task of utterance level emotion recognition in
conversations using commonsense knowledge. We propose COSMIC, a new framework
that incorporates different elements of commonsense such as mental states,
events, and causal relations, and build upon them to learn interactions between
interlocutors participating in a conversation. Current state-of-the-art methods
often encounter difficulties in context propagation, emotion shift detection,
and differentiating between related emotion classes. By learning distinct
commonsense representations, COSMIC addresses these challenges and achieves new
state-of-the-art results for emotion recognition on four different benchmark
conversational datasets. Our code is available at
https://github.com/declare-lab/conv-emotion.
| 2,020 | Computation and Language |
Tackling the Low-resource Challenge for Canonical Segmentation | Canonical morphological segmentation consists of dividing words into their
standardized morphemes. Here, we are interested in approaches for the task when
training data is limited. We compare model performance in a simulated
low-resource setting for the high-resource languages German, English, and
Indonesian to experiments on new datasets for the truly low-resource languages
Popoluca and Tepehua. We explore two new models for the task, borrowing from
the closely related area of morphological generation: an LSTM pointer-generator
and a sequence-to-sequence model with hard monotonic attention trained with
imitation learning. We find that, in the low-resource setting, the novel
approaches outperform existing ones on all languages by up to 11.4% accuracy.
However, while accuracy in emulated low-resource scenarios is over 50% for all
languages, for the truly low-resource languages Popoluca and Tepehua, our best
model only obtains 37.4% and 28.4% accuracy, respectively. Thus, we conclude
that canonical segmentation is still a challenging task for low-resource
languages.
| 2,020 | Computation and Language |
Textual Supervision for Visually Grounded Spoken Language Understanding | Visually-grounded models of spoken language understanding extract semantic
information directly from speech, without relying on transcriptions. This is
useful for low-resource languages, where transcriptions can be expensive or
impossible to obtain. Recent work showed that these models can be improved if
transcriptions are available at training time. However, it is not clear how an
end-to-end approach compares to a traditional pipeline-based approach when one
has access to transcriptions. Comparing different strategies, we find that the
pipeline approach works better when enough text is available. With low-resource
languages in mind, we also show that translations can be effectively used in
place of transcriptions but more data is needed to obtain similar results.
| 2,020 | Computation and Language |
Learning to Ignore: Long Document Coreference with Bounded Memory Neural
Networks | Long document coreference resolution remains a challenging task due to the
large memory and runtime requirements of current models. Recent work doing
incremental coreference resolution using just the global representation of
entities shows practical benefits but requires keeping all entities in memory,
which can be impractical for long documents. We argue that keeping all entities
in memory is unnecessary, and we propose a memory-augmented neural network that
tracks only a small bounded number of entities at a time, thus guaranteeing a
linear runtime in length of document. We show that (a) the model remains
competitive with models with high memory and computational requirements on
OntoNotes and LitBank, and (b) the model learns an efficient memory management
strategy easily outperforming a rule-based strategy.
| 2,020 | Computation and Language |
Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech
to Standard German Text Corpus | We present the Swiss Parliaments Corpus (SPC), an automatically aligned Swiss
German speech to Standard German text corpus. This first version of the corpus
is based on publicly available data of the Bernese cantonal parliament and
consists of 293 hours of data. It was created using a novel forced sentence
alignment procedure and an alignment quality estimator, which can be used to
trade off corpus size and quality. We trained Automatic Speech Recognition
(ASR) models as baselines on different subsets of the data and achieved a Word
Error Rate (WER) of 0.278 and a BLEU score of 0.586 on the SPC test set. The
corpus is freely available for download.
| 2,021 | Computation and Language |
Intrinsic Probing through Dimension Selection | Most modern NLP systems make use of pre-trained contextual representations
that attain astonishingly high performance on a variety of tasks. Such high
performance should not be possible unless some form of linguistic structure
inheres in these representations, and a wealth of research has sprung up on
probing for it. In this paper, we draw a distinction between intrinsic probing,
which examines how linguistic information is structured within a
representation, and the extrinsic probing popular in prior work, which only
argues for the presence of such information by showing that it can be
successfully extracted. To enable intrinsic probing, we propose a novel
framework based on a decomposable multivariate Gaussian probe that allows us to
determine whether the linguistic information in word embeddings is dispersed or
focal. We then probe fastText and BERT for various morphosyntactic attributes
across 36 languages. We find that most attributes are reliably encoded by only
a few neurons, with fastText concentrating its linguistic structure more than
BERT.
| 2,020 | Computation and Language |
QADiscourse -- Discourse Relations as QA Pairs: Representation,
Crowdsourcing and Baselines | Discourse relations describe how two propositions relate to one another, and
identifying them automatically is an integral part of natural language
understanding. However, annotating discourse relations typically requires
expert annotators. Recently, different semantic aspects of a sentence have been
represented and crowd-sourced via question-and-answer (QA) pairs. This paper
proposes a novel representation of discourse relations as QA pairs, which in
turn allows us to crowd-source wide-coverage data annotated with discourse
relations, via an intuitively appealing interface for composing such questions
and answers. Based on our proposed representation, we collect a novel and
wide-coverage QADiscourse dataset, and present baseline algorithms for
predicting QADiscourse relations.
| 2,020 | Computation and Language |
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