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Discontinuous Constituent Parsing as Sequence Labeling | This paper reduces discontinuous parsing to sequence labeling. It first shows
that existing reductions for constituent parsing as labeling do not support
discontinuities. Second, it fills this gap and proposes to encode tree
discontinuities as nearly ordered permutations of the input sequence. Third, it
studies whether such discontinuous representations are learnable. The
experiments show that despite the architectural simplicity, under the right
representation, the models are fast and accurate.
| 2,020 | Computation and Language |
Predicting User Engagement Status for Online Evaluation of Intelligent
Assistants | Evaluation of intelligent assistants in large-scale and online settings
remains an open challenge. User behavior-based online evaluation metrics have
demonstrated great effectiveness for monitoring large-scale web search and
recommender systems. Therefore, we consider predicting user engagement status
as the very first and critical step to online evaluation for intelligent
assistants. In this work, we first proposed a novel framework for classifying
user engagement status into four categories -- fulfillment, continuation,
reformulation and abandonment. We then demonstrated how to design simple but
indicative metrics based on the framework to quantify user engagement levels.
We also aim for automating user engagement prediction with machine learning
methods. We compare various models and features for predicting engagement
status using four real-world datasets. We conducted detailed analyses on
features and failure cases to discuss the performance of current models as well
as challenges.
| 2,021 | Computation and Language |
Learning Variational Word Masks to Improve the Interpretability of
Neural Text Classifiers | To build an interpretable neural text classifier, most of the prior work has
focused on designing inherently interpretable models or finding faithful
explanations. A new line of work on improving model interpretability has just
started, and many existing methods require either prior information or human
annotations as additional inputs in training. To address this limitation, we
propose the variational word mask (VMASK) method to automatically learn
task-specific important words and reduce irrelevant information on
classification, which ultimately improves the interpretability of model
predictions. The proposed method is evaluated with three neural text
classifiers (CNN, LSTM, and BERT) on seven benchmark text classification
datasets. Experiments show the effectiveness of VMASK in improving both model
prediction accuracy and interpretability.
| 2,020 | Computation and Language |
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting
Arithmetic Coding | Linguistic steganography studies how to hide secret messages in natural
language cover texts. Traditional methods aim to transform a secret message
into an innocent text via lexical substitution or syntactical modification.
Recently, advances in neural language models (LMs) enable us to directly
generate cover text conditioned on the secret message. In this study, we
present a new linguistic steganography method which encodes secret messages
using self-adjusting arithmetic coding based on a neural language model. We
formally analyze the statistical imperceptibility of this method and
empirically show it outperforms the previous state-of-the-art methods on four
datasets by 15.3% and 38.9% in terms of bits/word and KL metrics, respectively.
Finally, human evaluations show that 51% of generated cover texts can indeed
fool eavesdroppers.
| 2,020 | Computation and Language |
Beyond The Text: Analysis of Privacy Statements through Syntactic and
Semantic Role Labeling | This paper formulates a new task of extracting privacy parameters from a
privacy policy, through the lens of Contextual Integrity, an established social
theory framework for reasoning about privacy norms. Privacy policies, written
by lawyers, are lengthy and often comprise incomplete and vague statements. In
this paper, we show that traditional NLP tasks, including the recently proposed
Question-Answering based solutions, are insufficient to address the privacy
parameter extraction problem and provide poor precision and recall. We describe
4 different types of conventional methods that can be partially adapted to
address the parameter extraction task with varying degrees of success: Hidden
Markov Models, BERT fine-tuned models, Dependency Type Parsing (DP) and
Semantic Role Labeling (SRL). Based on a detailed evaluation across 36
real-world privacy policies of major enterprises, we demonstrate that a
solution combining syntactic DP coupled with type-specific SRL tasks provides
the highest accuracy for retrieving contextual privacy parameters from privacy
statements. We also observe that incorporating domain-specific knowledge is
critical to achieving high precision and recall, thus inspiring new NLP
research to address this important problem in the privacy domain.
| 2,020 | Computation and Language |
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and
Act in Fantasy Worlds | We seek to create agents that both act and communicate with other agents in
pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019) -- a
large-scale crowd-sourced fantasy text-game -- with a dataset of quests. These
contain natural language motivations paired with in-game goals and human
demonstrations; completing a quest might require dialogue or actions (or both).
We introduce a reinforcement learning system that (1) incorporates large-scale
language modeling-based and commonsense reasoning-based pre-training to imbue
the agent with relevant priors; and (2) leverages a factorized action space of
action commands and dialogue, balancing between the two. We conduct zero-shot
evaluations using held-out human expert demonstrations, showing that our agents
are able to act consistently and talk naturally with respect to their
motivations.
| 2,021 | Computation and Language |
Nearest Neighbor Machine Translation | We introduce $k$-nearest-neighbor machine translation ($k$NN-MT), which
predicts tokens with a nearest neighbor classifier over a large datastore of
cached examples, using representations from a neural translation model for
similarity search. This approach requires no additional training and scales to
give the decoder direct access to billions of examples at test time, resulting
in a highly expressive model that consistently improves performance across many
settings. Simply adding nearest neighbor search improves a state-of-the-art
German-English translation model by 1.5 BLEU. $k$NN-MT allows a single model to
be adapted to diverse domains by using a domain-specific datastore, improving
results by an average of 9.2 BLEU over zero-shot transfer, and achieving new
state-of-the-art results -- without training on these domains. A massively
multilingual model can also be specialized for particular language pairs, with
improvements of 3 BLEU for translating from English into German and Chinese.
Qualitatively, $k$NN-MT is easily interpretable; it combines source and target
context to retrieve highly relevant examples.
| 2,021 | Computation and Language |
A Survey of the State of Explainable AI for Natural Language Processing | Recent years have seen important advances in the quality of state-of-the-art
models, but this has come at the expense of models becoming less interpretable.
This survey presents an overview of the current state of Explainable AI (XAI),
considered within the domain of Natural Language Processing (NLP). We discuss
the main categorization of explanations, as well as the various ways
explanations can be arrived at and visualized. We detail the operations and
explainability techniques currently available for generating explanations for
NLP model predictions, to serve as a resource for model developers in the
community. Finally, we point out the current gaps and encourage directions for
future work in this important research area.
| 2,020 | Computation and Language |
STIL -- Simultaneous Slot Filling, Translation, Intent Classification,
and Language Identification: Initial Results using mBART on MultiATIS++ | Slot-filling, Translation, Intent classification, and Language
identification, or STIL, is a newly-proposed task for multilingual Natural
Language Understanding (NLU). By performing simultaneous slot filling and
translation into a single output language (English in this case), some portion
of downstream system components can be monolingual, reducing development and
maintenance cost. Results are given using the multilingual BART model (Liu et
al., 2020) fine-tuned on 7 languages using the MultiATIS++ dataset. When no
translation is performed, mBART's performance is comparable to the current
state of the art system (Cross-Lingual BERT by Xu et al. (2020)) for the
languages tested, with better average intent classification accuracy (96.07%
versus 95.50%) but worse average slot F1 (89.87% versus 90.81%). When
simultaneous translation is performed, average intent classification accuracy
degrades by only 1.7% relative and average slot F1 degrades by only 1.2%
relative.
| 2,020 | Computation and Language |
Enriching Word Embeddings with Temporal and Spatial Information | The meaning of a word is closely linked to sociocultural factors that can
change over time and location, resulting in corresponding meaning changes.
Taking a global view of words and their meanings in a widely used language,
such as English, may require us to capture more refined semantics for use in
time-specific or location-aware situations, such as the study of cultural
trends or language use. However, popular vector representations for words do
not adequately include temporal or spatial information. In this work, we
present a model for learning word representation conditioned on time and
location. In addition to capturing meaning changes over time and location, we
require that the resulting word embeddings retain salient semantic and
geometric properties. We train our model on time- and location-stamped corpora,
and show using both quantitative and qualitative evaluations that it can
capture semantics across time and locations. We note that our model compares
favorably with the state-of-the-art for time-specific embedding, and serves as
a new benchmark for location-specific embeddings.
| 2,020 | Computation and Language |
Enhancing Fine-grained Sentiment Classification Exploiting Local Context
Embedding | Target-oriented sentiment classification is a fine-grained task of natural
language processing to analyze the sentiment polarity of the targets. To
improve the performance of sentiment classification, many approaches proposed
various attention mechanisms to capture the important context words of a
target. However, previous approaches ignored the significant relatedness of a
target's sentiment and its local context. This paper proposes a local
context-aware network (LCA-Net), equipped with the local context embedding and
local context prediction loss, to strengthen the model by emphasizing the
sentiment information of the local context. The experimental results on three
common datasets show that local context-aware network performs superior to
existing approaches in extracting local context features. Besides, the local
context-aware framework is easy to adapt to many models, with the potential to
improve other target-level tasks.
| 2,021 | Computation and Language |
An Empirical Investigation Towards Efficient Multi-Domain Language Model
Pre-training | Pre-training large language models has become a standard in the natural
language processing community. Such models are pre-trained on generic data
(e.g. BookCorpus and English Wikipedia) and often fine-tuned on tasks in the
same domain. However, in order to achieve state-of-the-art performance on out
of domain tasks such as clinical named entity recognition and relation
extraction, additional in domain pre-training is required. In practice, staged
multi-domain pre-training presents performance deterioration in the form of
catastrophic forgetting (CF) when evaluated on a generic benchmark such as
GLUE. In this paper we conduct an empirical investigation into known methods to
mitigate CF. We find that elastic weight consolidation provides best overall
scores yielding only a 0.33% drop in performance across seven generic tasks
while remaining competitive in bio-medical tasks. Furthermore, we explore
gradient and latent clustering based data selection techniques to improve
coverage when using elastic weight consolidation and experience replay methods.
| 2,020 | Computation and Language |
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding | Knowledge graphs (KGs) contain rich information about world knowledge,
entities and relations. Thus, they can be great supplements to existing
pre-trained language models. However, it remains a challenge to efficiently
integrate information from KG into language modeling. And the understanding of
a knowledge graph requires related context. We propose a novel joint
pre-training framework, JAKET, to model both the knowledge graph and language.
The knowledge module and language module provide essential information to
mutually assist each other: the knowledge module produces embeddings for
entities in text while the language module generates context-aware initial
embeddings for entities and relations in the graph. Our design enables the
pre-trained model to easily adapt to unseen knowledge graphs in new domains.
Experimental results on several knowledge-aware NLP tasks show that our
proposed framework achieves superior performance by effectively leveraging
knowledge in language understanding.
| 2,020 | Computation and Language |
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge
Using Large-Scale Language Models | Existing pre-trained large language models have shown unparalleled generative
capabilities. However, they are not controllable. In this paper, we propose
MEGATRON-CNTRL, a novel framework that uses large-scale language models and
adds control to text generation by incorporating an external knowledge base.
Our framework consists of a keyword predictor, a knowledge retriever, a
contextual knowledge ranker, and a conditional text generator. As we do not
have access to ground-truth supervision for the knowledge ranker, we make use
of weak supervision from sentence embedding. The empirical results show that
our model generates more fluent, consistent, and coherent stories with less
repetition and higher diversity compared to prior work on the ROC story
dataset. We showcase the controllability of our model by replacing the keywords
used to generate stories and re-running the generation process. Human
evaluation results show that 77.5% of these stories are successfully controlled
by the new keywords. Furthermore, by scaling our model from 124 million to 8.3
billion parameters we demonstrate that larger models improve both the quality
of generation (from 74.5% to 93.0% for consistency) and controllability (from
77.5% to 91.5%).
| 2,020 | Computation and Language |
Which *BERT? A Survey Organizing Contextualized Encoders | Pretrained contextualized text encoders are now a staple of the NLP
community. We present a survey on language representation learning with the aim
of consolidating a series of shared lessons learned across a variety of recent
efforts. While significant advancements continue at a rapid pace, we find that
enough has now been discovered, in different directions, that we can begin to
organize advances according to common themes. Through this organization, we
highlight important considerations when interpreting recent contributions and
choosing which model to use.
| 2,020 | Computation and Language |
SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in
BERT-based Embedding Spaces | Lexical semantic change detection (also known as semantic shift tracing) is a
task of identifying words that have changed their meaning over time.
Unsupervised semantic shift tracing, focal point of SemEval2020, is
particularly challenging. Given the unsupervised setup, in this work, we
propose to identify clusters among different occurrences of each target word,
considering these as representatives of different word meanings. As such,
disagreements in obtained clusters naturally allow to quantify the level of
semantic shift per each target word in four target languages. To leverage this
idea, clustering is performed on contextualized (BERT-based) embeddings of word
occurrences. The obtained results show that our approach performs well both
measured separately (per language) and overall, where we surpass all provided
SemEval baselines.
| 2,020 | Computation and Language |
Autoregressive Entity Retrieval | Entities are at the center of how we represent and aggregate knowledge. For
instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one
per Wikipedia article). The ability to retrieve such entities given a query is
fundamental for knowledge-intensive tasks such as entity linking and
open-domain question answering. Current approaches can be understood as
classifiers among atomic labels, one for each entity. Their weight vectors are
dense entity representations produced by encoding entity meta information such
as their descriptions. This approach has several shortcomings: (i) context and
entity affinity is mainly captured through a vector dot product, potentially
missing fine-grained interactions; (ii) a large memory footprint is needed to
store dense representations when considering large entity sets; (iii) an
appropriately hard set of negative data has to be subsampled at training time.
In this work, we propose GENRE, the first system that retrieves entities by
generating their unique names, left to right, token-by-token in an
autoregressive fashion. This mitigates the aforementioned technical issues
since: (i) the autoregressive formulation directly captures relations between
context and entity name, effectively cross encoding both; (ii) the memory
footprint is greatly reduced because the parameters of our encoder-decoder
architecture scale with vocabulary size, not entity count; (iii) the softmax
loss is computed without subsampling negative data. We experiment with more
than 20 datasets on entity disambiguation, end-to-end entity linking and
document retrieval tasks, achieving new state-of-the-art or very competitive
results while using a tiny fraction of the memory footprint of competing
systems. Finally, we demonstrate that new entities can be added by simply
specifying their names. Code and pre-trained models at
https://github.com/facebookresearch/GENRE.
| 2,021 | Computation and Language |
Continual Learning for Natural Language Generation in Task-oriented
Dialog Systems | Natural language generation (NLG) is an essential component of task-oriented
dialog systems. Despite the recent success of neural approaches for NLG, they
are typically developed in an offline manner for particular domains. To better
fit real-life applications where new data come in a stream, we study NLG in a
"continual learning" setting to expand its knowledge to new domains or
functionalities incrementally. The major challenge towards this goal is
catastrophic forgetting, meaning that a continually trained model tends to
forget the knowledge it has learned before. To this end, we propose a method
called ARPER (Adaptively Regularized Prioritized Exemplar Replay) by replaying
prioritized historical exemplars, together with an adaptive regularization
technique based on ElasticWeight Consolidation. Extensive experiments to
continually learn new domains and intents are conducted on MultiWoZ-2.0 to
benchmark ARPER with a wide range of techniques. Empirical results demonstrate
that ARPER significantly outperforms other methods by effectively mitigating
the detrimental catastrophic forgetting issue.
| 2,020 | Computation and Language |
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on
a Massive Scale | We study the zero-shot transfer capabilities of text matching models on a
massive scale, by self-supervised training on 140 source domains from community
question answering forums in English. We investigate the model performances on
nine benchmarks of answer selection and question similarity tasks, and show
that all 140 models transfer surprisingly well, where the large majority of
models substantially outperforms common IR baselines. We also demonstrate that
considering a broad selection of source domains is crucial for obtaining the
best zero-shot transfer performances, which contrasts the standard procedure
that merely relies on the largest and most similar domains. In addition, we
extensively study how to best combine multiple source domains. We propose to
incorporate self-supervised with supervised multi-task learning on all
available source domains. Our best zero-shot transfer model considerably
outperforms in-domain BERT and the previous state of the art on six benchmarks.
Fine-tuning of our model with in-domain data results in additional large gains
and achieves the new state of the art on all nine benchmarks.
| 2,020 | Computation and Language |
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation | In the last few years, there has been a surge of interest in learning
representations of entitiesand relations in knowledge graph (KG). However, the
recent availability of temporal knowledgegraphs (TKGs) that contain time
information for each fact created the need for reasoning overtime in such TKGs.
In this regard, we present a new approach of TKG embedding, TeRo, which defines
the temporal evolution of entity embedding as a rotation from the initial time
to the currenttime in the complex vector space. Specially, for facts involving
time intervals, each relation isrepresented as a pair of dual complex
embeddings to handle the beginning and the end of therelation, respectively. We
show our proposed model overcomes the limitations of the existing KG embedding
models and TKG embedding models and has the ability of learning and
inferringvarious relation patterns over time. Experimental results on four
different TKGs show that TeRo significantly outperforms existing
state-of-the-art models for link prediction. In addition, we analyze the effect
of time granularity on link prediction over TKGs, which as far as we know
hasnot been investigated in previous literature.
| 2,020 | Computation and Language |
Unsupervised Text Style Transfer with Padded Masked Language Models | We propose Masker, an unsupervised text-editing method for style transfer. To
tackle cases when no parallel source-target pairs are available, we train
masked language models (MLMs) for both the source and the target domain. Then
we find the text spans where the two models disagree the most in terms of
likelihood. This allows us to identify the source tokens to delete to transform
the source text to match the style of the target domain. The deleted tokens are
replaced with the target MLM, and by using a padded MLM variant, we avoid
having to predetermine the number of inserted tokens. Our experiments on
sentence fusion and sentiment transfer demonstrate that Masker performs
competitively in a fully unsupervised setting. Moreover, in low-resource
settings, it improves supervised methods' accuracy by over 10 percentage points
when pre-training them on silver training data generated by Masker.
| 2,020 | Computation and Language |
LUKE: Deep Contextualized Entity Representations with Entity-aware
Self-attention | Entity representations are useful in natural language tasks involving
entities. In this paper, we propose new pretrained contextualized
representations of words and entities based on the bidirectional transformer.
The proposed model treats words and entities in a given text as independent
tokens, and outputs contextualized representations of them. Our model is
trained using a new pretraining task based on the masked language model of
BERT. The task involves predicting randomly masked words and entities in a
large entity-annotated corpus retrieved from Wikipedia. We also propose an
entity-aware self-attention mechanism that is an extension of the
self-attention mechanism of the transformer, and considers the types of tokens
(words or entities) when computing attention scores. The proposed model
achieves impressive empirical performance on a wide range of entity-related
tasks. In particular, it obtains state-of-the-art results on five well-known
datasets: Open Entity (entity typing), TACRED (relation classification),
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering),
and SQuAD 1.1 (extractive question answering). Our source code and pretrained
representations are available at https://github.com/studio-ousia/luke.
| 2,020 | Computation and Language |
Data-Efficient Pretraining via Contrastive Self-Supervision | For natural language processing `text-to-text' tasks, the prevailing
approaches heavily rely on pretraining large self-supervised models on
increasingly larger `task-external' data. Transfer learning from high-resource
pretraining works well, but research has focused on settings with very large
data and compute requirements, while the potential of efficient low-resource
learning, without large `task-external' pretraining, remains under-explored. In
this work, we evaluate against three core challenges for resource efficient
learning. Namely, we analyze: (1) pretraining data ($X$) efficiency; (2) zero
to few-shot label ($Y$) efficiency; and (3) long-tail generalization, since
long-tail preservation has been linked to algorithmic fairness and because data
in the tail is limited by definition. To address these challenges, we propose a
data and compute efficient self-supervised, contrastive text encoder,
pretrained on 60MB of `task-internal' text data, and compare it to RoBERTa,
which was pretrained on 160GB of `task-external' text. We find our method
outperforms RoBERTa, while pretraining and fine-tuning in a 1/5th of RoBERTa's
fine-tuning time.
| 2,021 | Computation and Language |
Syntax Representation in Word Embeddings and Neural Networks -- A Survey | Neural networks trained on natural language processing tasks capture syntax
even though it is not provided as a supervision signal. This indicates that
syntactic analysis is essential to the understating of language in artificial
intelligence systems. This overview paper covers approaches of evaluating the
amount of syntactic information included in the representations of words for
different neural network architectures. We mainly summarize re-search on
English monolingual data on language modeling tasks and multilingual data for
neural machine translation systems and multilingual language models. We
describe which pre-trained models and representations of language are best
suited for transfer to syntactic tasks.
| 2,020 | Computation and Language |
HUMAN: Hierarchical Universal Modular ANnotator | A lot of real-world phenomena are complex and cannot be captured by single
task annotations. This causes a need for subsequent annotations, with
interdependent questions and answers describing the nature of the subject at
hand. Even in the case a phenomenon is easily captured by a single task, the
high specialisation of most annotation tools can result in having to switch to
another tool if the task only slightly changes.
We introduce HUMAN, a novel web-based annotation tool that addresses the
above problems by a) covering a variety of annotation tasks on both textual and
image data, and b) the usage of an internal deterministic state machine,
allowing the researcher to chain different annotation tasks in an
interdependent manner. Further, the modular nature of the tool makes it easy to
define new annotation tasks and integrate machine learning algorithms e.g., for
active learning. HUMAN comes with an easy-to-use graphical user interface that
simplifies the annotation task and management.
| 2,020 | Computation and Language |
Multi-Modal Open-Domain Dialogue | Recent work in open-domain conversational agents has demonstrated that
significant improvements in model engagingness and humanness metrics can be
achieved via massive scaling in both pre-training data and model size
(Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build
agents with human-like abilities, we must expand beyond handling just text. A
particularly important topic is the ability to see images and communicate about
what is perceived. With the goal of engaging humans in multi-modal dialogue, we
investigate combining components from state-of-the-art open-domain dialogue
agents with those from state-of-the-art vision models. We study incorporating
different image fusion schemes and domain-adaptive pre-training and fine-tuning
strategies, and show that our best resulting model outperforms strong existing
models in multi-modal dialogue while simultaneously performing as well as its
predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based
conversation. We additionally investigate and incorporate safety components in
our final model, and show that such efforts do not diminish model performance
with respect to engagingness metrics.
| 2,020 | Computation and Language |
Cross-Lingual Transfer Learning for Complex Word Identification | Complex Word Identification (CWI) is a task centered on detecting
hard-to-understand words, or groups of words, in texts from different areas of
expertise. The purpose of CWI is to highlight problematic structures that
non-native speakers would usually find difficult to understand. Our approach
uses zero-shot, one-shot, and few-shot learning techniques, alongside
state-of-the-art solutions for Natural Language Processing (NLP) tasks (i.e.,
Transformers). Our aim is to provide evidence that the proposed models can
learn the characteristics of complex words in a multilingual environment by
relying on the CWI shared task 2018 dataset available for four different
languages (i.e., English, German, Spanish, and also French). Our approach
surpasses state-of-the-art cross-lingual results in terms of macro F1-score on
English (0.774), German (0.782), and Spanish (0.734) languages, for the
zero-shot learning scenario. At the same time, our model also outperforms the
state-of-the-art monolingual result for German (0.795 macro F1-score).
| 2,020 | Computation and Language |
Cost-effective Selection of Pretraining Data: A Case Study of
Pretraining BERT on Social Media | Recent studies on domain-specific BERT models show that effectiveness on
downstream tasks can be improved when models are pretrained on in-domain data.
Often, the pretraining data used in these models are selected based on their
subject matter, e.g., biology or computer science. Given the range of
applications using social media text, and its unique language variety, we
pretrain two models on tweets and forum text respectively, and empirically
demonstrate the effectiveness of these two resources. In addition, we
investigate how similarity measures can be used to nominate in-domain
pretraining data. We publicly release our pretrained models at
https://bit.ly/35RpTf0.
| 2,020 | Computation and Language |
Automatic Extraction of Rules Governing Morphological Agreement | Creating a descriptive grammar of a language is an indispensable step for
language documentation and preservation. However, at the same time it is a
tedious, time-consuming task. In this paper, we take steps towards automating
this process by devising an automated framework for extracting a first-pass
grammatical specification from raw text in a concise, human- and
machine-readable format. We focus on extracting rules describing agreement, a
morphosyntactic phenomenon at the core of the grammars of many of the world's
languages. We apply our framework to all languages included in the Universal
Dependencies project, with promising results. Using cross-lingual transfer,
even with no expert annotations in the language of interest, our framework
extracts a grammatical specification which is nearly equivalent to those
created with large amounts of gold-standard annotated data. We confirm this
finding with human expert evaluations of the rules that our framework produces,
which have an average accuracy of 78%. We release an interface demonstrating
the extracted rules at https://neulab.github.io/lase/.
| 2,020 | Computation and Language |
Multi-domain Clinical Natural Language Processing with MedCAT: the
Medical Concept Annotation Toolkit | Electronic health records (EHR) contain large volumes of unstructured text,
requiring the application of Information Extraction (IE) technologies to enable
clinical analysis. We present the open-source Medical Concept Annotation
Toolkit (MedCAT) that provides: a) a novel self-supervised machine learning
algorithm for extracting concepts using any concept vocabulary including
UMLS/SNOMED-CT; b) a feature-rich annotation interface for customising and
training IE models; and c) integrations to the broader CogStack ecosystem for
vendor-agnostic health system deployment. We show improved performance in
extracting UMLS concepts from open datasets (F1:0.448-0.738 vs 0.429-0.650).
Further real-world validation demonstrates SNOMED-CT extraction at 3 large
London hospitals with self-supervised training over ~8.8B words from ~17M
clinical records and further fine-tuning with ~6K clinician annotated examples.
We show strong transferability (F1 > 0.94) between hospitals, datasets, and
concept types indicating cross-domain EHR-agnostic utility for accelerated
clinical and research use cases.
| 2,021 | Computation and Language |
DocuBot : Generating financial reports using natural language
interactions | The financial services industry perpetually processes an overwhelming amount
of complex data. Digital reports are often created based on tedious manual
analysis as well as visualization of the underlying trends and characteristics
of data. Often, the accruing costs of human computation errors in creating
these reports are very high. We present DocuBot, a novel AI-powered virtual
assistant for creating and modifying content in digital documents by modeling
natural language interactions as "skills" and using them to transform
underlying data. DocuBot has the ability to agglomerate saved skills for reuse,
enabling humans to automatically generate recurrent reports. DocuBot also has
the capability to continuously learn domain-specific and user-specific
vocabulary by interacting with the user. We present evidence that DocuBot adds
value to the financial industry and demonstrate its impact with experiments
involving real and simulated users tasked with creating PowerPoint
presentations.
| 2,021 | Computation and Language |
Mining Knowledge for Natural Language Inference from Wikipedia
Categories | Accurate lexical entailment (LE) and natural language inference (NLI) often
require large quantities of costly annotations. To alleviate the need for
labeled data, we introduce WikiNLI: a resource for improving model performance
on NLI and LE tasks. It contains 428,899 pairs of phrases constructed from
naturally annotated category hierarchies in Wikipedia. We show that we can
improve strong baselines such as BERT and RoBERTa by pretraining them on
WikiNLI and transferring the models on downstream tasks. We conduct systematic
comparisons with phrases extracted from other knowledge bases such as WordNet
and Wikidata to find that pretraining on WikiNLI gives the best performance. In
addition, we construct WikiNLI in other languages, and show that pretraining on
them improves performance on NLI tasks of corresponding languages.
| 2,020 | Computation and Language |
Multilevel Text Alignment with Cross-Document Attention | Text alignment finds application in tasks such as citation recommendation and
plagiarism detection. Existing alignment methods operate at a single,
predefined level and cannot learn to align texts at, for example, sentence and
document levels. We propose a new learning approach that equips previously
established hierarchical attention encoders for representing documents with a
cross-document attention component, enabling structural comparisons across
different levels (document-to-document and sentence-to-document). Our component
is weakly supervised from document pairs and can align at multiple levels. Our
evaluation on predicting document-to-document relationships and
sentence-to-document relationships on the tasks of citation recommendation and
plagiarism detection shows that our approach outperforms previously established
hierarchical, attention encoders based on recurrent and transformer
contextualization that are unaware of structural correspondence between
documents.
| 2,020 | Computation and Language |
Partially-Aligned Data-to-Text Generation with Distant Supervision | The Data-to-Text task aims to generate human-readable text for describing
some given structured data enabling more interpretability. However, the typical
generation task is confined to a few particular domains since it requires
well-aligned data which is difficult and expensive to obtain. Using
partially-aligned data is an alternative way of solving the dataset scarcity
problem. This kind of data is much easier to obtain since it can be produced
automatically. However, using this kind of data induces the over-generation
problem posing difficulties for existing models, which tends to add unrelated
excerpts during the generation procedure. In order to effectively utilize
automatically annotated partially-aligned datasets, we extend the traditional
generation task to a refined task called Partially-Aligned Data-to-Text
Generation (PADTG) which is more practical since it utilizes automatically
annotated data for training and thus considerably expands the application
domains. To tackle this new task, we propose a novel distant supervision
generation framework. It firstly estimates the input data's supportiveness for
each target word with an estimator and then applies a supportiveness adaptor
and a rebalanced beam search to harness the over-generation problem in the
training and generation phases respectively. We also contribute a
partially-aligned dataset (The data and source code of this paper can be
obtained from https://github.com/fuzihaofzh/distant_supervision_nlg by sampling
sentences from Wikipedia and automatically extracting corresponding KB triples
for each sentence from Wikidata. The experimental results show that our
framework outperforms all baseline models as well as verify the feasibility of
utilizing partially-aligned data.
| 2,020 | Computation and Language |
Towards Interpretable Reasoning over Paragraph Effects in Situation | We focus on the task of reasoning over paragraph effects in situation, which
requires a model to understand the cause and effect described in a background
paragraph, and apply the knowledge to a novel situation. Existing works ignore
the complicated reasoning process and solve it with a one-step "black box"
model. Inspired by human cognitive processes, in this paper we propose a
sequential approach for this task which explicitly models each step of the
reasoning process with neural network modules. In particular, five reasoning
modules are designed and learned in an end-to-end manner, which leads to a more
interpretable model. Experimental results on the ROPES dataset demonstrate the
effectiveness and explainability of our proposed approach.
| 2,020 | Computation and Language |
UNISON: Unpaired Cross-lingual Image Captioning | Image captioning has emerged as an interesting research field in recent years
due to its broad application scenarios. The traditional paradigm of image
captioning relies on paired image-caption datasets to train the model in a
supervised manner. However, creating such paired datasets for every target
language is prohibitively expensive, which hinders the extensibility of
captioning technology and deprives a large part of the world population of its
benefit. In this work, we present a novel unpaired cross-lingual method to
generate image captions without relying on any caption corpus in the source or
the target language. Specifically, our method consists of two phases: (i) a
cross-lingual auto-encoding process, which utilizing a sentence parallel
(bitext) corpus to learn the mapping from the source to the target language in
the scene graph encoding space and decode sentences in the target language, and
(ii) a cross-modal unsupervised feature mapping, which seeks to map the encoded
scene graph features from image modality to language modality. We verify the
effectiveness of our proposed method on the Chinese image caption generation
task. The comparisons against several existing methods demonstrate the
effectiveness of our approach.
| 2,022 | Computation and Language |
Personality Trait Detection Using Bagged SVM over BERT Word Embedding
Ensembles | Recently, the automatic prediction of personality traits has received
increasing attention and has emerged as a hot topic within the field of
affective computing. In this work, we present a novel deep learning-based
approach for automated personality detection from text. We leverage state of
the art advances in natural language understanding, namely the BERT language
model to extract contextualized word embeddings from textual data for automated
author personality detection. Our primary goal is to develop a computationally
efficient, high-performance personality prediction model which can be easily
used by a large number of people without access to huge computation resources.
Our extensive experiments with this ideology in mind, led us to develop a novel
model which feeds contextualized embeddings along with psycholinguistic
features toa Bagged-SVM classifier for personality trait prediction. Our model
outperforms the previous state of the art by 1.04% and, at the same time is
significantly more computationally efficient to train. We report our results on
the famous gold standard Essays dataset for personality detection.
| 2,020 | Computation and Language |
A Geometry-Inspired Attack for Generating Natural Language Adversarial
Examples | Generating adversarial examples for natural language is hard, as natural
language consists of discrete symbols, and examples are often of variable
lengths. In this paper, we propose a geometry-inspired attack for generating
natural language adversarial examples. Our attack generates adversarial
examples by iteratively approximating the decision boundary of Deep Neural
Networks (DNNs). Experiments on two datasets with two different models show
that our attack fools natural language models with high success rates, while
only replacing a few words. Human evaluation shows that adversarial examples
generated by our attack are hard for humans to recognize. Further experiments
show that adversarial training can improve model robustness against our attack.
| 2,020 | Computation and Language |
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter | For multi-turn dialogue rewriting, the capacity of effectively modeling the
linguistic knowledge in dialog context and getting rid of the noises is
essential to improve its performance. Existing attentive models attend to all
words without prior focus, which results in inaccurate concentration on some
dispensable words. In this paper, we propose to use semantic role labeling
(SRL), which highlights the core semantic information of who did what to whom,
to provide additional guidance for the rewriter model. Experiments show that
this information significantly improves a RoBERTa-based model that already
outperforms previous state-of-the-art systems.
| 2,020 | Computation and Language |
Aspect-Based Sentiment Analysis in Education Domain | Analysis of a large amount of data has always brought value to institutions
and organizations. Lately, people's opinions expressed through text have become
a very important aspect of this analysis. In response to this challenge, a
natural language processing technique known as Aspect-Based Sentiment Analysis
(ABSA) has emerged. Having the ability to extract the polarity for each aspect
of opinions separately, ABSA has found itself useful in a wide range of
domains. Education is one of the domains in which ABSA can be successfully
utilized. Being able to understand and find out what students like and don't
like most about a course, professor, or teaching methodology can be of great
importance for the respective institutions. While this task represents a unique
NLP challenge, many studies have proposed different approaches to tackle the
problem. In this work, we present a comprehensive review of the existing work
in ABSA with a focus in the education domain. A wide range of methodologies are
discussed and conclusions are drawn.
| 2,020 | Computation and Language |
GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented
Dialogue Systems | End-to-end task-oriented dialogue systems aim to generate system responses
directly from plain text inputs. There are two challenges for such systems: one
is how to effectively incorporate external knowledge bases (KBs) into the
learning framework; the other is how to accurately capture the semantics of
dialogue history. In this paper, we address these two challenges by exploiting
the graph structural information in the knowledge base and in the dependency
parsing tree of the dialogue. To effectively leverage the structural
information in dialogue history, we propose a new recurrent cell architecture
which allows representation learning on graphs. To exploit the relations
between entities in KBs, the model combines multi-hop reasoning ability based
on the graph structure. Experimental results show that the proposed model
achieves consistent improvement over state-of-the-art models on two different
task-oriented dialogue datasets.
| 2,020 | Computation and Language |
MIME: MIMicking Emotions for Empathetic Response Generation | Current approaches to empathetic response generation view the set of emotions
expressed in the input text as a flat structure, where all the emotions are
treated uniformly. We argue that empathetic responses often mimic the emotion
of the user to a varying degree, depending on its positivity or negativity and
content. We show that the consideration of this polarity-based emotion clusters
and emotional mimicry results in improved empathy and contextual relevance of
the response as compared to the state-of-the-art. Also, we introduce
stochasticity into the emotion mixture that yields emotionally more varied
empathetic responses than the previous work. We demonstrate the importance of
these factors to empathetic response generation using both automatic- and
human-based evaluations. The implementation of MIME is publicly available at
https://github.com/declare-lab/MIME.
| 2,020 | Computation and Language |
Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph
Attention Networks | Aspect category sentiment analysis (ACSA) aims to predict the sentiment
polarities of the aspect categories discussed in sentences. Since a sentence
usually discusses one or more aspect categories and expresses different
sentiments toward them, various attention-based methods have been developed to
allocate the appropriate sentiment words for the given aspect category and
obtain promising results. However, most of these methods directly use the given
aspect category to find the aspect category-related sentiment words, which may
cause mismatching between the sentiment words and the aspect categories when an
unrelated sentiment word is semantically meaningful for the given aspect
category. To mitigate this problem, we propose a Sentence Constituent-Aware
Network (SCAN) for aspect-category sentiment analysis. SCAN contains two graph
attention modules and an interactive loss function. The graph attention modules
generate representations of the nodes in sentence constituency parse trees for
the aspect category detection (ACD) task and the ACSA task, respectively. ACD
aims to detect aspect categories discussed in sentences and is a auxiliary
task. For a given aspect category, the interactive loss function helps the ACD
task to find the nodes which can predict the aspect category but can't predict
other aspect categories. The sentiment words in the nodes then are used to
predict the sentiment polarity of the aspect category by the ACSA task. The
experimental results on five public datasets demonstrate the effectiveness of
SCAN.
| 2,020 | Computation and Language |
Tell Me How to Ask Again: Question Data Augmentation with Controllable
Rewriting in Continuous Space | In this paper, we propose a novel data augmentation method, referred to as
Controllable Rewriting based Question Data Augmentation (CRQDA), for machine
reading comprehension (MRC), question generation, and question-answering
natural language inference tasks. We treat the question data augmentation task
as a constrained question rewriting problem to generate context-relevant,
high-quality, and diverse question data samples. CRQDA utilizes a Transformer
autoencoder to map the original discrete question into a continuous embedding
space. It then uses a pre-trained MRC model to revise the question
representation iteratively with gradient-based optimization. Finally, the
revised question representations are mapped back into the discrete space, which
serve as additional question data. Comprehensive experiments on SQuAD 2.0,
SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of
CRQDA
| 2,020 | Computation and Language |
Knowledge-Enhanced Personalized Review Generation with Capsule Graph
Neural Network | Personalized review generation (PRG) aims to automatically produce review
text reflecting user preference, which is a challenging natural language
generation task. Most of previous studies do not explicitly model factual
description of products, tending to generate uninformative content. Moreover,
they mainly focus on word-level generation, but cannot accurately reflect more
abstractive user preference in multiple aspects. To address the above issues,
we propose a novel knowledge-enhanced PRG model based on capsule graph neural
network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG)
for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules
for encoding underlying characteristics from the HKG. Our generation process
contains two major steps, namely aspect sequence generation and sentence
generation. First, based on graph capsules, we adaptively learn aspect capsules
for inferring the aspect sequence. Then, conditioned on the inferred aspect
label, we design a graph-based copy mechanism to generate sentences by
incorporating related entities or words from HKG. To our knowledge, we are the
first to utilize knowledge graph for the PRG task. The incorporated KG
information is able to enhance user preference at both aspect and word levels.
Extensive experiments on three real-world datasets have demonstrated the
effectiveness of our model on the PRG task.
| 2,020 | Computation and Language |
Paragraph-level Commonsense Transformers with Recurrent Memory | Human understanding of narrative texts requires making commonsense inferences
beyond what is stated explicitly in the text. A recent model, COMET, can
generate such implicit commonsense inferences along several dimensions such as
pre- and post-conditions, motivations, and mental states of the participants.
However, COMET was trained on commonsense inferences of short phrases, and is
therefore discourse-agnostic. When presented with each sentence of a
multi-sentence narrative, it might generate inferences that are inconsistent
with the rest of the narrative.
We present the task of discourse-aware commonsense inference. Given a
sentence within a narrative, the goal is to generate commonsense inferences
along predefined dimensions, while maintaining coherence with the rest of the
narrative. Such large-scale paragraph-level annotation is hard to get and
costly, so we use available sentence-level annotations to efficiently and
automatically construct a distantly supervised corpus.
Using this corpus, we train PARA-COMET, a discourse-aware model that
incorporates paragraph-level information to generate coherent commonsense
inferences from narratives. PARA-COMET captures both semantic knowledge
pertaining to prior world knowledge, and episodic knowledge involving how
current events relate to prior and future events in a narrative. Our results
show that PARA-COMET outperforms the sentence-level baselines, particularly in
generating inferences that are both coherent and novel.
| 2,021 | Computation and Language |
Dialogue Generation on Infrequent Sentence Functions via Structured
Meta-Learning | Sentence function is an important linguistic feature indicating the
communicative purpose in uttering a sentence. Incorporating sentence functions
into conversations has shown improvements in the quality of generated
responses. However, the number of utterances for different types of
fine-grained sentence functions is extremely imbalanced. Besides a small number
of high-resource sentence functions, a large portion of sentence functions is
infrequent. Consequently, dialogue generation conditioned on these infrequent
sentence functions suffers from data deficiency. In this paper, we investigate
a structured meta-learning (SML) approach for dialogue generation on infrequent
sentence functions. We treat dialogue generation conditioned on different
sentence functions as separate tasks, and apply model-agnostic meta-learning to
high-resource sentence functions data. Furthermore, SML enhances meta-learning
effectiveness by promoting knowledge customization among different sentence
functions but simultaneously preserving knowledge generalization for similar
sentence functions. Experimental results demonstrate that SML not only improves
the informativeness and relevance of generated responses, but also can generate
responses consistent with the target sentence functions.
| 2,020 | Computation and Language |
Explaining Deep Neural Networks | Deep neural networks are becoming more and more popular due to their
revolutionary success in diverse areas, such as computer vision, natural
language processing, and speech recognition. However, the decision-making
processes of these models are generally not interpretable to users. In various
domains, such as healthcare, finance, or law, it is critical to know the
reasons behind a decision made by an artificial intelligence system. Therefore,
several directions for explaining neural models have recently been explored. In
this thesis, I investigate two major directions for explaining deep neural
networks. The first direction consists of feature-based post-hoc explanatory
methods, that is, methods that aim to explain an already trained and fixed
model (post-hoc), and that provide explanations in terms of input features,
such as tokens for text and superpixels for images (feature-based). The second
direction consists of self-explanatory neural models that generate natural
language explanations, that is, models that have a built-in module that
generates explanations for the predictions of the model.
| 2,021 | Computation and Language |
Multi-turn Response Selection using Dialogue Dependency Relations | Multi-turn response selection is a task designed for developing dialogue
agents. The performance on this task has a remarkable improvement with
pre-trained language models. However, these models simply concatenate the turns
in dialogue history as the input and largely ignore the dependencies between
the turns. In this paper, we propose a dialogue extraction algorithm to
transform a dialogue history into threads based on their dependency relations.
Each thread can be regarded as a self-contained sub-dialogue. We also propose
Thread-Encoder model to encode threads and candidates into compact
representations by pre-trained Transformers and finally get the matching score
through an attention layer. The experiments show that dependency relations are
helpful for dialogue context understanding, and our model outperforms the
state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results
on UbuntuV2.
| 2,020 | Computation and Language |
A Multi-task Learning Framework for Opinion Triplet Extraction | The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are
mainly based on either detecting aspect terms and their corresponding sentiment
polarities, or co-extracting aspect and opinion terms. However, the extraction
of aspect-sentiment pairs lacks opinion terms as a reference, while
co-extraction of aspect and opinion terms would not lead to meaningful pairs
without determining their sentiment dependencies. To address the issue, we
present a novel view of ABSA as an opinion triplet extraction task, and propose
a multi-task learning framework to jointly extract aspect terms and opinion
terms, and simultaneously parses sentiment dependencies between them with a
biaffine scorer. At inference phase, the extraction of triplets is facilitated
by a triplet decoding method based on the above outputs. We evaluate the
proposed framework on four SemEval benchmarks for ASBA. The results demonstrate
that our approach significantly outperforms a range of strong baselines and
state-of-the-art approaches.
| 2,020 | Computation and Language |
A Survey of Unsupervised Dependency Parsing | Syntactic dependency parsing is an important task in natural language
processing. Unsupervised dependency parsing aims to learn a dependency parser
from sentences that have no annotation of their correct parse trees. Despite
its difficulty, unsupervised parsing is an interesting research direction
because of its capability of utilizing almost unlimited unannotated text data.
It also serves as the basis for other research in low-resource parsing. In this
paper, we survey existing approaches to unsupervised dependency parsing,
identify two major classes of approaches, and discuss recent trends. We hope
that our survey can provide insights for researchers and facilitate future
research on this topic.
| 2,020 | Computation and Language |
Leveraging Multilingual News Websites for Building a Kurdish Parallel
Corpus | Machine translation has been a major motivation of development in natural
language processing. Despite the burgeoning achievements in creating more
efficient machine translation systems thanks to deep learning methods, parallel
corpora have remained indispensable for progress in the field. In an attempt to
create parallel corpora for the Kurdish language, in this paper, we describe
our approach in retrieving potentially-alignable news articles from
multi-language websites and manually align them across dialects and languages
based on lexical similarity and transliteration of scripts. We present a corpus
containing 12,327 translation pairs in the two major dialects of Kurdish,
Sorani and Kurmanji. We also provide 1,797 and 650 translation pairs in
English-Kurmanji and English-Sorani. The corpus is publicly available under the
CC BY-NC-SA 4.0 license.
| 2,020 | Computation and Language |
Reverse Operation based Data Augmentation for Solving Math Word Problems | Automatically solving math word problems is a critical task in the field of
natural language processing. Recent models have reached their performance
bottleneck and require more high-quality data for training. We propose a novel
data augmentation method that reverses the mathematical logic of math word
problems to produce new high-quality math problems and introduce new knowledge
points that can benefit learning the mathematical reasoning logic. We apply the
augmented data on two SOTA math word problem solving models and compare our
results with a strong data augmentation baseline. Experimental results show the
effectiveness of our approach. We release our code and data at
https://github.com/yiyunya/RODA.
| 2,021 | Computation and Language |
Adversarial Attack and Defense of Structured Prediction Models | Building an effective adversarial attacker and elaborating on countermeasures
for adversarial attacks for natural language processing (NLP) have attracted a
lot of research in recent years. However, most of the existing approaches focus
on classification problems. In this paper, we investigate attacks and defenses
for structured prediction tasks in NLP. Besides the difficulty of perturbing
discrete words and the sentence fluency problem faced by attackers in any NLP
tasks, there is a specific challenge to attackers of structured prediction
models: the structured output of structured prediction models is sensitive to
small perturbations in the input. To address these problems, we propose a novel
and unified framework that learns to attack a structured prediction model using
a sequence-to-sequence model with feedbacks from multiple reference models of
the same structured prediction task. Based on the proposed attack, we further
reinforce the victim model with adversarial training, making its prediction
more robust and accurate. We evaluate the proposed framework in dependency
parsing and part-of-speech tagging. Automatic and human evaluations show that
our proposed framework succeeds in both attacking state-of-the-art structured
prediction models and boosting them with adversarial training.
| 2,020 | Computation and Language |
When in Doubt, Ask: Generating Answerable and Unanswerable Questions,
Unsupervised | Question Answering (QA) is key for making possible a robust communication
between human and machine. Modern language models used for QA have surpassed
the human-performance in several essential tasks; however, these models require
large amounts of human-generated training data which are costly and
time-consuming to create. This paper studies augmenting human-made datasets
with synthetic data as a way of surmounting this problem. A state-of-the-art
model based on deep transformers is used to inspect the impact of using
synthetic answerable and unanswerable questions to complement a well-known
human-made dataset. The results indicate a tangible improvement in the
performance of the language model (measured in terms of F1 and EM scores)
trained on the mixed dataset. Specifically, unanswerable question-answers prove
more effective in boosting the model: the F1 score gain from adding to the
original dataset the answerable, unanswerable, and combined question-answers
were 1.3%, 5.0%, and 6.7%, respectively. [Link to the Github repository:
https://github.com/lnikolenko/EQA]
| 2,021 | Computation and Language |
Meta Sequence Learning for Generating Adequate Question-Answer Pairs | Creating multiple-choice questions to assess reading comprehension of a given
article involves generating question-answer pairs (QAPs) on the main points of
the document. We present a learning scheme to generate adequate QAPs via
meta-sequence representations of sentences. A meta sequence is a sequence of
vectors comprising semantic and syntactic tags. In particular, we devise a
scheme called MetaQA to learn meta sequences from training data to form pairs
of a meta sequence for a declarative sentence (MD) and a corresponding
interrogative sentence (MIs). On a given declarative sentence, a trained MetaQA
model converts it to a meta sequence, finds a matched MD, and uses the
corresponding MIs and the input sentence to generate QAPs. We implement MetaQA
for the English language using semantic-role labeling, part-of-speech tagging,
and named-entity recognition, and show that trained on a small dataset, MetaQA
generates efficiently over the official SAT practice reading tests a large
number of syntactically and semantically correct QAPs with over 97\% accuracy.
| 2,021 | Computation and Language |
An Empirical Study on Large-Scale Multi-Label Text Classification
Including Few and Zero-Shot Labels | Large-scale Multi-label Text Classification (LMTC) has a wide range of
Natural Language Processing (NLP) applications and presents interesting
challenges. First, not all labels are well represented in the training set, due
to the very large label set and the skewed label distributions of LMTC
datasets. Also, label hierarchies and differences in human labelling guidelines
may affect graph-aware annotation proximity. Finally, the label hierarchies are
periodically updated, requiring LMTC models capable of zero-shot
generalization. Current state-of-the-art LMTC models employ Label-Wise
Attention Networks (LWANs), which (1) typically treat LMTC as flat multi-label
classification; (2) may use the label hierarchy to improve zero-shot learning,
although this practice is vastly understudied; and (3) have not been combined
with pre-trained Transformers (e.g. BERT), which have led to state-of-the-art
results in several NLP benchmarks. Here, for the first time, we empirically
evaluate a battery of LMTC methods from vanilla LWANs to hierarchical
classification approaches and transfer learning, on frequent, few, and
zero-shot learning on three datasets from different domains. We show that
hierarchical methods based on Probabilistic Label Trees (PLTs) outperform
LWANs. Furthermore, we show that Transformer-based approaches outperform the
state-of-the-art in two of the datasets, and we propose a new state-of-the-art
method which combines BERT with LWANs. Finally, we propose new models that
leverage the label hierarchy to improve few and zero-shot learning, considering
on each dataset a graph-aware annotation proximity measure that we introduce.
| 2,020 | Computation and Language |
Inquisitive Question Generation for High Level Text Comprehension | Inquisitive probing questions come naturally to humans in a variety of
settings, but is a challenging task for automatic systems. One natural type of
question to ask tries to fill a gap in knowledge during text comprehension,
like reading a news article: we might ask about background information, deeper
reasons behind things occurring, or more. Despite recent progress with
data-driven approaches, generating such questions is beyond the range of models
trained on existing datasets.
We introduce INQUISITIVE, a dataset of ~19K questions that are elicited while
a person is reading through a document. Compared to existing datasets,
INQUISITIVE questions target more towards high-level (semantic and discourse)
comprehension of text. We show that readers engage in a series of pragmatic
strategies to seek information. Finally, we evaluate question generation models
based on GPT-2 and show that our model is able to generate reasonable questions
although the task is challenging, and highlight the importance of context to
generate INQUISITIVE questions.
| 2,020 | Computation and Language |
Generating Dialogue Responses from a Semantic Latent Space | Existing open-domain dialogue generation models are usually trained to mimic
the gold response in the training set using cross-entropy loss on the
vocabulary. However, a good response does not need to resemble the gold
response, since there are multiple possible responses to a given prompt. In
this work, we hypothesize that the current models are unable to integrate
information from multiple semantically similar valid responses of a prompt,
resulting in the generation of generic and uninformative responses. To address
this issue, we propose an alternative to the end-to-end classification on
vocabulary. We learn the pair relationship between the prompts and responses as
a regression task on a latent space instead. In our novel dialog generation
model, the representations of semantically related sentences are close to each
other on the latent space. Human evaluation showed that learning the task on a
continuous space can generate responses that are both relevant and informative.
| 2,020 | Computation and Language |
Improving Target-side Lexical Transfer in Multilingual Neural Machine
Translation | To improve the performance of Neural Machine Translation~(NMT) for
low-resource languages~(LRL), one effective strategy is to leverage parallel
data from a related high-resource language~(HRL). However, multilingual data
has been found more beneficial for NMT models that translate from the LRL to a
target language than the ones that translate into the LRLs. In this paper, we
aim to improve the effectiveness of multilingual transfer for NMT models that
translate \emph{into} the LRL, by designing a better decoder word embedding.
Extending upon a general-purpose multilingual encoding method Soft Decoupled
Encoding~\citep{SDE}, we propose DecSDE, an efficient character n-gram based
embedding specifically designed for the NMT decoder. Our experiments show that
DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English
to four different languages.
| 2,020 | Computation and Language |
Multi-View Sequence-to-Sequence Models with Conversational Structure for
Abstractive Dialogue Summarization | Text summarization is one of the most challenging and interesting problems in
NLP. Although much attention has been paid to summarizing structured text like
news reports or encyclopedia articles, summarizing conversations---an essential
part of human-human/machine interaction where most important pieces of
information are scattered across various utterances of different
speakers---remains relatively under-investigated. This work proposes a
multi-view sequence-to-sequence model by first extracting conversational
structures of unstructured daily chats from different views to represent
conversations and then utilizing a multi-view decoder to incorporate different
views to generate dialogue summaries. Experiments on a large-scale dialogue
summarization corpus demonstrated that our methods significantly outperformed
previous state-of-the-art models via both automatic evaluations and human
judgment. We also discussed specific challenges that current approaches faced
with this task. We have publicly released our code at
https://github.com/GT-SALT/Multi-View-Seq2Seq.
| 2,020 | Computation and Language |
Local Additivity Based Data Augmentation for Semi-supervised NER | Named Entity Recognition (NER) is one of the first stages in deep language
understanding yet current NER models heavily rely on human-annotated data. In
this work, to alleviate the dependence on labeled data, we propose a Local
Additivity based Data Augmentation (LADA) method for semi-supervised NER, in
which we create virtual samples by interpolating sequences close to each other.
Our approach has two variations: Intra-LADA and Inter-LADA, where Intra-LADA
performs interpolations among tokens within one sentence, and Inter-LADA
samples different sentences to interpolate. Through linear additions between
sampled training data, LADA creates an infinite amount of labeled data and
improves both entity and context learning. We further extend LADA to the
semi-supervised setting by designing a novel consistency loss for unlabeled
data. Experiments conducted on two NER benchmarks demonstrate the effectiveness
of our methods over several strong baselines. We have publicly released our
code at https://github.com/GT-SALT/LADA.
| 2,020 | Computation and Language |
Optimal Neural Program Synthesis from Multimodal Specifications | Multimodal program synthesis, which leverages different types of user input
to synthesize a desired program, is an attractive way to scale program
synthesis to challenging settings; however, it requires integrating noisy
signals from the user, like natural language, with hard constraints on the
program's behavior. This paper proposes an optimal neural synthesis approach
where the goal is to find a program that satisfies user-provided constraints
while also maximizing the program's score with respect to a neural model.
Specifically, we focus on multimodal synthesis tasks in which the user intent
is expressed using a combination of natural language (NL) and input-output
examples. At the core of our method is a top-down recurrent neural model that
places distributions over abstract syntax trees conditioned on the NL input.
This model not only allows for efficient search over the space of syntactically
valid programs, but it allows us to leverage automated program analysis
techniques for pruning the search space based on infeasibility of partial
programs with respect to the user's constraints. The experimental results on a
multimodal synthesis dataset (StructuredRegex) show that our method
substantially outperforms prior state-of-the-art techniques in terms of
accuracy and efficiency, and finds model-optimal programs more frequently.
| 2,021 | Computation and Language |
Weakly-supervised Fine-grained Event Recognition on Social Media Texts
for Disaster Management | People increasingly use social media to report emergencies, seek help or
share information during disasters, which makes social networks an important
tool for disaster management. To meet these time-critical needs, we present a
weakly supervised approach for rapidly building high-quality classifiers that
label each individual Twitter message with fine-grained event categories. Most
importantly, we propose a novel method to create high-quality labeled data in a
timely manner that automatically clusters tweets containing an event keyword
and asks a domain expert to disambiguate event word senses and label clusters
quickly. In addition, to process extremely noisy and often rather short
user-generated messages, we enrich tweet representations using preceding
context tweets and reply tweets in building event recognition classifiers. The
evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2
person-hours of human supervision, the rapidly trained weakly supervised
classifiers outperform supervised classifiers trained using more than ten
thousand annotated tweets created in over 50 person-hours.
| 2,020 | Computation and Language |
DLGNet-Task: An End-to-end Neural Network Framework for Modeling
Multi-turn Multi-domain Task-Oriented Dialogue | Task oriented dialogue (TOD) requires the complex interleaving of a number of
individually controllable components with strong guarantees for explainability
and verifiability. This has made it difficult to adopt the multi-turn
multi-domain dialogue generation capabilities of streamlined end-to-end
open-domain dialogue systems. In this paper, we present a new framework,
DLGNet-Task, a unified task-oriented dialogue system which employs
autoregressive transformer networks such as DLGNet and GPT-2/3 to complete user
tasks in multi-turn multi-domain conversations. Our framework enjoys the
controllable, verifiable, and explainable outputs of modular approaches, and
the low development, deployment and maintenance cost of end-to-end systems.
Treating open-domain system components as additional TOD system modules allows
DLGNet-Task to learn the joint distribution of the inputs and outputs of all
the functional blocks of existing modular approaches such as, natural language
understanding (NLU), state tracking, action policy, as well as natural language
generation (NLG). Rather than training the modules individually, as is common
in real-world systems, we trained them jointly with appropriate module
separations. When evaluated on the MultiWOZ2.1 dataset, DLGNet-Task shows
comparable performance to the existing state-of-the-art approaches.
Furthermore, using DLGNet-Task in conversational AI systems reduces the level
of effort required for developing, deploying, and maintaining intelligent
assistants at scale.
| 2,020 | Computation and Language |
On Losses for Modern Language Models | BERT set many state-of-the-art results over varied NLU benchmarks by
pre-training over two tasks: masked language modelling (MLM) and next sentence
prediction (NSP), the latter of which has been highly criticized. In this
paper, we 1) clarify NSP's effect on BERT pre-training, 2) explore fourteen
possible auxiliary pre-training tasks, of which seven are novel to modern
language models, and 3) investigate different ways to include multiple tasks
into pre-training. We show that NSP is detrimental to training due to its
context splitting and shallow semantic signal. We also identify six auxiliary
pre-training tasks -- sentence ordering, adjacent sentence prediction, TF
prediction, TF-IDF prediction, a FastSent variant, and a Quick Thoughts variant
-- that outperform a pure MLM baseline. Finally, we demonstrate that using
multiple tasks in a multi-task pre-training framework provides better results
than using any single auxiliary task. Using these methods, we outperform BERT
Base on the GLUE benchmark using fewer than a quarter of the training tokens.
| 2,020 | Computation and Language |
Reading Comprehension as Natural Language Inference: A Semantic Analysis | In the recent past, Natural language Inference (NLI) has gained significant
attention, particularly given its promise for downstream NLP tasks. However,
its true impact is limited and has not been well studied. Therefore, in this
paper, we explore the utility of NLI for one of the most prominent downstream
tasks, viz. Question Answering (QA). We transform the one of the largest
available MRC dataset (RACE) to an NLI form, and compare the performances of a
state-of-the-art model (RoBERTa) on both these forms. We propose new
characterizations of questions, and evaluate the performance of QA and NLI
models on these categories. We highlight clear categories for which the model
is able to perform better when the data is presented in a coherent entailment
form, and a structured question-answer concatenation form, respectively.
| 2,020 | Computation and Language |
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story
Generation | Systems for story generation are asked to produce plausible and enjoyable
stories given an input context. This task is underspecified, as a vast number
of diverse stories can originate from a single input. The large output space
makes it difficult to build and evaluate story generation models, as (1)
existing datasets lack rich enough contexts to meaningfully guide models, and
(2) existing evaluations (both crowdsourced and automatic) are unreliable for
assessing long-form creative text. To address these issues, we introduce a
dataset and evaluation platform built from STORIUM, an online collaborative
storytelling community. Our author-generated dataset contains 6K lengthy
stories (125M tokens) with fine-grained natural language annotations (e.g.,
character goals and attributes) interspersed throughout each narrative, forming
a robust source for guiding models. We evaluate language models fine-tuned on
our dataset by integrating them onto STORIUM, where real authors can query a
model for suggested story continuations and then edit them. Automatic metrics
computed over these edits correlate well with both user ratings of generated
stories and qualitative feedback from semi-structured user interviews. We
release both the STORIUM dataset and evaluation platform to spur more
principled research into story generation.
| 2,020 | Computation and Language |
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning | Multi-hop reasoning approaches over knowledge graphs infer a missing
relationship between entities with a multi-hop rule, which corresponds to a
chain of relationships. We extend existing works to consider a generalized form
of multi-hop rules, where each rule is a set of relation chains. To learn such
generalized rules efficiently, we propose a two-step approach that first
selects a small set of relation chains as a rule and then evaluates the
confidence of the target relationship by jointly scoring the selected chains. A
game-theoretical framework is proposed to this end to simultaneously optimize
the rule selection and prediction steps. Empirical results show that our
multi-chain multi-hop (MCMH) rules result in superior results compared to the
standard single-chain approaches, justifying both our formulation of
generalized rules and the effectiveness of the proposed learning framework.
| 2,020 | Computation and Language |
Transformer-Based Neural Text Generation with Syntactic Guidance | We study the problem of using (partial) constituency parse trees as syntactic
guidance for controlled text generation. Existing approaches to this problem
use recurrent structures, which not only suffer from the long-term dependency
problem but also falls short in modeling the tree structure of the syntactic
guidance. We propose to leverage the parallelism of Transformer to better
incorporate parse trees. Our method first expands a partial template
constituency parse tree to a full-fledged parse tree tailored for the input
source text, and then uses the expanded tree to guide text generation. The
effectiveness of our model in this process hinges upon two new attention
mechanisms: 1) a path attention mechanism that forces one node to attend to
only other nodes located in its path in the syntax tree to better incorporate
syntax guidance; 2) a multi-encoder attention mechanism that allows the decoder
to dynamically attend to information from multiple encoders. Our experiments in
the controlled paraphrasing task show that our method outperforms SOTA models
both semantically and syntactically, improving the best baseline's BLEU score
from 11.83 to 26.27.
| 2,020 | Computation and Language |
Effective Unsupervised Domain Adaptation with Adversarially Trained
Language Models | Recent work has shown the importance of adaptation of broad-coverage
contextualised embedding models on the domain of the target task of interest.
Current self-supervised adaptation methods are simplistic, as the training
signal comes from a small percentage of \emph{randomly} masked-out tokens. In
this paper, we show that careful masking strategies can bridge the knowledge
gap of masked language models (MLMs) about the domains more effectively by
allocating self-supervision where it is needed. Furthermore, we propose an
effective training strategy by adversarially masking out those tokens which are
harder to reconstruct by the underlying MLM. The adversarial objective leads to
a challenging combinatorial optimisation problem over \emph{subsets} of tokens,
which we tackle efficiently through relaxation to a variational lowerbound and
dynamic programming. On six unsupervised domain adaptation tasks involving
named entity recognition, our method strongly outperforms the random masking
strategy and achieves up to +1.64 F1 score improvements.
| 2,020 | Computation and Language |
On the Effects of Knowledge-Augmented Data in Word Embeddings | This paper investigates techniques for knowledge injection into word
embeddings learned from large corpora of unannotated data. These
representations are trained with word cooccurrence statistics and do not
commonly exploit syntactic and semantic information from linguistic knowledge
bases, which potentially limits their transferability to domains with differing
language distributions or usages. We propose a novel approach for linguistic
knowledge injection through data augmentation to learn word embeddings that
enforce semantic relationships from the data, and systematically evaluate the
impact it has on the resulting representations. We show our knowledge
augmentation approach improves the intrinsic characteristics of the learned
embeddings while not significantly altering their results on a downstream text
classification task.
| 2,020 | Computation and Language |
Second-Order NLP Adversarial Examples | Adversarial example generation methods in NLP rely on models like language
models or sentence encoders to determine if potential adversarial examples are
valid. In these methods, a valid adversarial example fools the model being
attacked, and is determined to be semantically or syntactically valid by a
second model. Research to date has counted all such examples as errors by the
attacked model. We contend that these adversarial examples may not be flaws in
the attacked model, but flaws in the model that determines validity. We term
such invalid inputs second-order adversarial examples. We propose the
constraint robustness curve and associated metric ACCS as tools for evaluating
the robustness of a constraint to second-order adversarial examples. To
generate this curve, we design an adversarial attack to run directly on the
semantic similarity models. We test on two constraints, the Universal Sentence
Encoder (USE) and BERTScore. Our findings indicate that such second-order
examples exist, but are typically less common than first-order adversarial
examples in state-of-the-art models. They also indicate that USE is effective
as constraint on NLP adversarial examples, while BERTScore is nearly
ineffectual. Code for running the experiments in this paper is available at
https://github.com/jxmorris12/second-order-adversarial-examples.
| 2,020 | Computation and Language |
Improving AMR Parsing with Sequence-to-Sequence Pre-training | In the literature, the research on abstract meaning representation (AMR)
parsing is much restricted by the size of human-curated dataset which is
critical to build an AMR parser with good performance. To alleviate such data
size restriction, pre-trained models have been drawing more and more attention
in AMR parsing. However, previous pre-trained models, like BERT, are
implemented for general purpose which may not work as expected for the specific
task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq)
AMR parsing and propose a seq2seq pre-training approach to build pre-trained
models in both single and joint way on three relevant tasks, i.e., machine
translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the
vanilla fine-tuning method to a multi-task learning fine-tuning method that
optimizes for the performance of AMR parsing while endeavors to preserve the
response of pre-trained models. Extensive experimental results on two English
benchmark datasets show that both the single and joint pre-trained models
significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0),
which reaches the state of the art. The result is very encouraging since we
achieve this with seq2seq models rather than complex models. We make our code
and model available at https://github.com/xdqkid/S2S-AMR-Parser.
| 2,020 | Computation and Language |
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive
Learning | Evaluation of a document summarization system has been a critical factor to
impact the success of the summarization task. Previous approaches, such as
ROUGE, mainly consider the informativeness of the assessed summary and require
human-generated references for each test summary. In this work, we propose to
evaluate the summary qualities without reference summaries by unsupervised
contrastive learning. Specifically, we design a new metric which covers both
linguistic qualities and semantic informativeness based on BERT. To learn the
metric, for each summary, we construct different types of negative samples with
respect to different aspects of the summary qualities, and train our model with
a ranking loss. Experiments on Newsroom and CNN/Daily Mail demonstrate that our
new evaluation method outperforms other metrics even without reference
summaries. Furthermore, we show that our method is general and transferable
across datasets.
| 2,020 | Computation and Language |
Corpora Evaluation and System Bias Detection in Multi-document
Summarization | Multi-document summarization (MDS) is the task of reflecting key points from
any set of documents into a concise text paragraph. In the past, it has been
used to aggregate news, tweets, product reviews, etc. from various sources.
Owing to no standard definition of the task, we encounter a plethora of
datasets with varying levels of overlap and conflict between participating
documents. There is also no standard regarding what constitutes summary
information in MDS. Adding to the challenge is the fact that new systems report
results on a set of chosen datasets, which might not correlate with their
performance on the other datasets. In this paper, we study this heterogeneous
task with the help of a few widely used MDS corpora and a suite of
state-of-the-art models. We make an attempt to quantify the quality of
summarization corpus and prescribe a list of points to consider while proposing
a new MDS corpus. Next, we analyze the reason behind the absence of an MDS
system which achieves superior performance across all corpora. We then observe
the extent to which system metrics are influenced, and bias is propagated due
to corpus properties. The scripts to reproduce the experiments in this work are
available at https://github.com/LCS2-IIITD/summarization_bias.git.
| 2,020 | Computation and Language |
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized
Identity Prior | Traditional (unstructured) pruning methods for a Transformer model focus on
regularizing the individual weights by penalizing them toward zero. In this
work, we explore spectral-normalized identity priors (SNIP), a structured
pruning approach that penalizes an entire residual module in a Transformer
model toward an identity mapping. Our method identifies and discards
unimportant non-linear mappings in the residual connections by applying a
thresholding operator on the function norm. It is applicable to any structured
module, including a single attention head, an entire attention block, or a
feed-forward subnetwork. Furthermore, we introduce spectral normalization to
stabilize the distribution of the post-activation values of the Transformer
layers, further improving the pruning effectiveness of the proposed
methodology. We conduct experiments with BERT on 5 GLUE benchmark tasks to
demonstrate that SNIP achieves effective pruning results while maintaining
comparable performance. Specifically, we improve the performance over the
state-of-the-art by 0.5 to 1.0% on average at 50% compression ratio.
| 2,020 | Computation and Language |
GenAug: Data Augmentation for Finetuning Text Generators | In this paper, we investigate data augmentation for text generation, which we
call GenAug. Text generation and language modeling are important tasks within
natural language processing, and are especially challenging for low-data
regimes. We propose and evaluate various augmentation methods, including some
that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp
Reviews. We also examine the relationship between the amount of augmentation
and the quality of the generated text. We utilize several metrics that evaluate
important aspects of the generated text including its diversity and fluency.
Our experiments demonstrate that insertion of character-level synthetic noise
and keyword replacement with hypernyms are effective augmentation methods, and
that the quality of generations improves to a peak at approximately three times
the amount of original data.
| 2,020 | Computation and Language |
On the Frailty of Universal POS Tags for Neural UD Parsers | We present an analysis on the effect UPOS accuracy has on parsing
performance. Results suggest that leveraging UPOS tags as features for neural
parsers requires a prohibitively high tagging accuracy and that the use of gold
tags offers a non-linear increase in performance, suggesting some sort of
exceptionality. We also investigate what aspects of predicted UPOS tags impact
parsing accuracy the most, highlighting some potentially meaningful linguistic
facets of the problem.
| 2,020 | Computation and Language |
Discern: Discourse-Aware Entailment Reasoning Network for Conversational
Machine Reading | Document interpretation and dialog understanding are the two major challenges
for conversational machine reading. In this work, we propose Discern, a
discourse-aware entailment reasoning network to strengthen the connection and
enhance the understanding for both document and dialog. Specifically, we split
the document into clause-like elementary discourse units (EDU) using a
pre-trained discourse segmentation model, and we train our model in a
weakly-supervised manner to predict whether each EDU is entailed by the user
feedback in a conversation. Based on the learned EDU and entailment
representations, we either reply to the user our final decision
"yes/no/irrelevant" of the initial question, or generate a follow-up question
to inquiry more information. Our experiments on the ShARC benchmark (blind,
held-out test set) show that Discern achieves state-of-the-art results of 78.3%
macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question
generation. Code and models are released at
https://github.com/Yifan-Gao/Discern.
| 2,020 | Computation and Language |
Linguistic Profiling of a Neural Language Model | In this paper we investigate the linguistic knowledge learned by a Neural
Language Model (NLM) before and after a fine-tuning process and how this
knowledge affects its predictions during several classification problems. We
use a wide set of probing tasks, each of which corresponds to a distinct
sentence-level feature extracted from different levels of linguistic
annotation. We show that BERT is able to encode a wide range of linguistic
characteristics, but it tends to lose this information when trained on specific
downstream tasks. We also find that BERT's capacity to encode different kind of
linguistic properties has a positive influence on its predictions: the more it
stores readable linguistic information of a sentence, the higher will be its
capacity of predicting the expected label assigned to that sentence.
| 2,020 | Computation and Language |
"LazImpa": Lazy and Impatient neural agents learn to communicate
efficiently | Previous work has shown that artificial neural agents naturally develop
surprisingly non-efficient codes. This is illustrated by the fact that in a
referential game involving a speaker and a listener neural networks optimizing
accurate transmission over a discrete channel, the emergent messages fail to
achieve an optimal length. Furthermore, frequent messages tend to be longer
than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA)
observed in all natural languages. Here, we show that near-optimal and
ZLA-compatible messages can emerge, but only if both the speaker and the
listener are modified. We hence introduce a new communication system,
"LazImpa", where the speaker is made increasingly lazy, i.e. avoids long
messages, and the listener impatient, i.e.,~seeks to guess the intended content
as soon as possible.
| 2,020 | Computation and Language |
A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese | Semantic parsing is an important NLP task. However, Vietnamese is a
low-resource language in this research area. In this paper, we present the
first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese.
We extend and evaluate two strong semantic parsing baselines EditSQL (Zhang et
al., 2019) and IRNet (Guo et al., 2019) on our dataset. We compare the two
baselines with key configurations and find that: automatic Vietnamese word
segmentation improves the parsing results of both baselines; the normalized
pointwise mutual information (NPMI) score (Bouma, 2009) is useful for schema
linking; latent syntactic features extracted from a neural dependency parser
for Vietnamese also improve the results; and the monolingual language model
PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) helps produce higher
performances than the recent best multilingual language model XLM-R (Conneau et
al., 2020).
| 2,020 | Computation and Language |
Regularizing Dialogue Generation by Imitating Implicit Scenarios | Human dialogues are scenario-based and appropriate responses generally relate
to the latent context knowledge entailed by the specific scenario. To enable
responses that are more meaningful and context-specific, we propose to improve
generative dialogue systems from the scenario perspective, where both dialogue
history and future conversation are taken into account to implicitly
reconstruct the scenario knowledge. More importantly, the conversation
scenarios are further internalized using imitation learning framework, where
the conventional dialogue model that has no access to future conversations is
effectively regularized by transferring the scenario knowledge contained in
hierarchical supervising signals from the scenario-based dialogue model, so
that the future conversation is not required in actual inference. Extensive
evaluations show that our approach significantly outperforms state-of-the-art
baselines on diversity and relevance, and expresses scenario-specific
knowledge.
| 2,020 | Computation and Language |
PUM at SemEval-2020 Task 12: Aggregation of Transformer-based models'
features for offensive language recognition | In this paper, we describe the PUM team's entry to the SemEval-2020 Task 12.
Creating our solution involved leveraging two well-known pretrained models used
in natural language processing: BERT and XLNet, which achieve state-of-the-art
results in multiple NLP tasks. The models were fine-tuned for each subtask
separately and features taken from their hidden layers were combined and fed
into a fully connected neural network. The model using aggregated Transformer
features can serve as a powerful tool for offensive language identification
problem. Our team was ranked 7th out of 40 in Sub-task C - Offense target
identification with 64.727% macro F1-score and 64th out of 85 in Sub-task A -
Offensive language identification (89.726% F1-score).
| 2,020 | Computation and Language |
Exploring Semantic Capacity of Terms | We introduce and study semantic capacity of terms. For example, the semantic
capacity of artificial intelligence is higher than that of linear regression
since artificial intelligence possesses a broader meaning scope. Understanding
semantic capacity of terms will help many downstream tasks in natural language
processing. For this purpose, we propose a two-step model to investigate
semantic capacity of terms, which takes a large text corpus as input and can
evaluate semantic capacity of terms if the text corpus can provide enough
co-occurrence information of terms. Extensive experiments in three fields
demonstrate the effectiveness and rationality of our model compared with
well-designed baselines and human-level evaluations.
| 2,020 | Computation and Language |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse
Knowledge Graph | Multi-hop reasoning has been widely studied in recent years to seek an
effective and interpretable method for knowledge graph (KG) completion. Most
previous reasoning methods are designed for dense KGs with enough paths between
entities, but cannot work well on those sparse KGs that only contain sparse
paths for reasoning. On the one hand, sparse KGs contain less information,
which makes it difficult for the model to choose correct paths. On the other
hand, the lack of evidential paths to target entities also makes the reasoning
process difficult. To solve these problems, we propose a multi-hop reasoning
model named DacKGR over sparse KGs, by applying novel dynamic anticipation and
completion strategies: (1) The anticipation strategy utilizes the latent
prediction of embedding-based models to make our model perform more potential
path search over sparse KGs. (2) Based on the anticipation information, the
completion strategy dynamically adds edges as additional actions during the
path search, which further alleviates the sparseness problem of KGs. The
experimental results on five datasets sampled from Freebase, NELL and Wikidata
show that our method outperforms state-of-the-art baselines. Our codes and
datasets can be obtained from https://github.com/THU-KEG/DacKGR
| 2,020 | Computation and Language |
Exploiting Unsupervised Data for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) aims to predict the emotional
state of speakers in conversations, which is essentially a text classification
task. Unlike the sentence-level text classification problem, the available
supervised data for the ERC task is limited, which potentially prevents the
models from playing their maximum effect. In this paper, we propose a novel
approach to leverage unsupervised conversation data, which is more accessible.
Specifically, we propose the Conversation Completion (ConvCom) task, which
attempts to select the correct answer from candidate answers to fill a masked
utterance in a conversation. Then, we Pre-train a basic COntext- Dependent
Encoder (Pre-CODE) on the ConvCom task. Finally, we fine-tune the Pre-CODE on
the datasets of ERC. Experimental results demonstrate that pre-training on
unsupervised data achieves significant improvement of performance on the ERC
datasets, particularly on the minority emotion classes.
| 2,020 | Computation and Language |
Learning from Context or Names? An Empirical Study on Neural Relation
Extraction | Neural models have achieved remarkable success on relation extraction (RE)
benchmarks. However, there is no clear understanding which type of information
affects existing RE models to make decisions and how to further improve the
performance of these models. To this end, we empirically study the effect of
two main information sources in text: textual context and entity mentions
(names). We find that (i) while context is the main source to support the
predictions, RE models also heavily rely on the information from entity
mentions, most of which is type information, and (ii) existing datasets may
leak shallow heuristics via entity mentions and thus contribute to the high
performance on RE benchmarks. Based on the analyses, we propose an
entity-masked contrastive pre-training framework for RE to gain a deeper
understanding on both textual context and type information while avoiding rote
memorization of entities or use of superficial cues in mentions. We carry out
extensive experiments to support our views, and show that our framework can
improve the effectiveness and robustness of neural models in different RE
scenarios. All the code and datasets are released at
https://github.com/thunlp/RE-Context-or-Names.
| 2,020 | Computation and Language |
X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset | Even though SRL is researched for many languages, major improvements have
mostly been obtained for English, for which more resources are available. In
fact, existing multilingual SRL datasets contain disparate annotation styles or
come from different domains, hampering generalization in multilingual learning.
In this work, we propose a method to automatically construct an SRL corpus that
is parallel in four languages: English, French, German, Spanish, with unified
predicate and role annotations that are fully comparable across languages. We
apply high-quality machine translation to the English CoNLL-09 dataset and use
multilingual BERT to project its high-quality annotations to the target
languages. We include human-validated test sets that we use to measure the
projection quality, and show that projection is denser and more precise than a
strong baseline. Finally, we train different SOTA models on our novel corpus
for mono- and multilingual SRL, showing that the multilingual annotations
improve performance especially for the weaker languages.
| 2,020 | Computation and Language |
Assessing Robustness of Text Classification through Maximal Safe Radius
Computation | Neural network NLP models are vulnerable to small modifications of the input
that maintain the original meaning but result in a different prediction. In
this paper, we focus on robustness of text classification against word
substitutions, aiming to provide guarantees that the model prediction does not
change if a word is replaced with a plausible alternative, such as a synonym.
As a measure of robustness, we adopt the notion of the maximal safe radius for
a given input text, which is the minimum distance in the embedding space to the
decision boundary. Since computing the exact maximal safe radius is not
feasible in practice, we instead approximate it by computing a lower and upper
bound. For the upper bound computation, we employ Monte Carlo Tree Search in
conjunction with syntactic filtering to analyse the effect of single and
multiple word substitutions. The lower bound computation is achieved through an
adaptation of the linear bounding techniques implemented in tools CNN-Cert and
POPQORN, respectively for convolutional and recurrent network models. We
evaluate the methods on sentiment analysis and news classification models for
four datasets (IMDB, SST, AG News and NEWS) and a range of embeddings, and
provide an analysis of robustness trends. We also apply our framework to
interpretability analysis and compare it with LIME.
| 2,020 | Computation and Language |
Gender prediction using limited Twitter Data | Transformer models have shown impressive performance on a variety of NLP
tasks. Off-the-shelf, pre-trained models can be fine-tuned for specific NLP
classification tasks, reducing the need for large amounts of additional
training data. However, little research has addressed how much data is required
to accurately fine-tune such pre-trained transformer models, and how much data
is needed for accurate prediction. This paper explores the usability of BERT (a
Transformer model for word embedding) for gender prediction on social media.
Forensic applications include detecting gender obfuscation, e.g. males posing
as females in chat rooms. A Dutch BERT model is fine-tuned on different samples
of a Dutch Twitter dataset labeled for gender, varying in the number of tweets
used per person. The results show that finetuning BERT contributes to good
gender classification performance (80% F1) when finetuned on only 200 tweets
per person. But when using just 20 tweets per person, the performance of our
classifier deteriorates non-steeply (to 70% F1). These results show that even
with relatively small amounts of data, BERT can be fine-tuned to accurately
help predict the gender of Twitter users, and, consequently, that it is
possible to determine gender on the basis of just a low volume of tweets. This
opens up an operational perspective on the swift detection of gender.
| 2,020 | Computation and Language |
Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells | We introduce Latent Meaning Cells, a deep latent variable model which learns
contextualized representations of words by combining local lexical context and
metadata. Metadata can refer to granular context, such as section type, or to
more global context, such as unique document ids. Reliance on metadata for
contextualized representation learning is apropos in the clinical domain where
text is semi-structured and expresses high variation in topics. We evaluate the
LMC model on the task of zero-shot clinical acronym expansion across three
datasets. The LMC significantly outperforms a diverse set of baselines at a
fraction of the pre-training cost and learns clinically coherent
representations. We demonstrate that not only is metadata itself very helpful
for the task, but that the LMC inference algorithm provides an additional large
benefit.
| 2,020 | Computation and Language |
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class
Classification | Label inventories for fine-grained entity typing have grown in size and
complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic
spaces offer a mathematically appealing approach for learning hierarchical
representations of symbolic data. However, it is not clear how to integrate
hyperbolic components into downstream tasks. This is the first work that
proposes a fully hyperbolic model for multi-class multi-label classification,
which performs all operations in hyperbolic space. We evaluate the proposed
model on two challenging datasets and compare to different baselines that
operate under Euclidean assumptions. Our hyperbolic model infers the latent
hierarchy from the class distribution, captures implicit hyponymic relations in
the inventory, and shows performance on par with state-of-the-art methods on
fine-grained classification with remarkable reduction of the parameter size. A
thorough analysis sheds light on the impact of each component in the final
prediction and showcases its ease of integration with Euclidean layers.
| 2,020 | Computation and Language |
Modulated Fusion using Transformer for Linguistic-Acoustic Emotion
Recognition | This paper aims to bring a new lightweight yet powerful solution for the task
of Emotion Recognition and Sentiment Analysis. Our motivation is to propose two
architectures based on Transformers and modulation that combine the linguistic
and acoustic inputs from a wide range of datasets to challenge, and sometimes
surpass, the state-of-the-art in the field. To demonstrate the efficiency of
our models, we carefully evaluate their performances on the IEMOCAP, MOSI,
MOSEI and MELD dataset. The experiments can be directly replicated and the code
is fully open for future researches.
| 2,020 | Computation and Language |
The Grammar of Emergent Languages | In this paper, we consider the syntactic properties of languages emerged in
referential games, using unsupervised grammar induction (UGI) techniques
originally designed to analyse natural language. We show that the considered
UGI techniques are appropriate to analyse emergent languages and we then study
if the languages that emerge in a typical referential game setup exhibit
syntactic structure, and to what extent this depends on the maximum message
length and number of symbols that the agents are allowed to use. Our
experiments demonstrate that a certain message length and vocabulary size are
required for structure to emerge, but they also illustrate that more
sophisticated game scenarios are required to obtain syntactic properties more
akin to those observed in human language. We argue that UGI techniques should
be part of the standard toolkit for analysing emergent languages and release a
comprehensive library to facilitate such analysis for future researchers.
| 2,020 | Computation and Language |
Gauravarora@HASOC-Dravidian-CodeMix-FIRE2020: Pre-training ULMFiT on
Synthetically Generated Code-Mixed Data for Hate Speech Detection | This paper describes the system submitted to Dravidian-Codemix-HASOC2020:
Hate Speech and Offensive Content Identification in Dravidian languages
(Tamil-English and Malayalam-English). The task aims to identify offensive
language in code-mixed dataset of comments/posts in Dravidian languages
collected from social media. We participated in both Sub-task A, which aims to
identify offensive content in mixed-script (mixture of Native and Roman script)
and Sub-task B, which aims to identify offensive content in Roman script, for
Dravidian languages. In order to address these tasks, we proposed pre-training
ULMFiT on synthetically generated code-mixed data, generated by modelling
code-mixed data generation as a Markov process using Markov chains. Our model
achieved 0.88 weighted F1-score for code-mixed Tamil-English language in
Sub-task B and got 2nd rank on the leader-board. Additionally, our model
achieved 0.91 weighted F1-score (4th Rank) for mixed-script Malayalam-English
in Sub-task A and 0.74 weighted F1-score (5th Rank) for code-mixed
Malayalam-English language in Sub-task B.
| 2,020 | Computation and Language |
Explaining The Efficacy of Counterfactually Augmented Data | In attempts to produce ML models less reliant on spurious patterns in NLP
datasets, researchers have recently proposed curating counterfactually
augmented data (CAD) via a human-in-the-loop process in which given some
documents and their (initial) labels, humans must revise the text to make a
counterfactual label applicable. Importantly, edits that are not necessary to
flip the applicable label are prohibited. Models trained on the augmented data
appear, empirically, to rely less on semantically irrelevant words and to
generalize better out of domain. While this work draws loosely on causal
thinking, the underlying causal model (even at an abstract level) and the
principles underlying the observed out-of-domain improvements remain unclear.
In this paper, we introduce a toy analog based on linear Gaussian models,
observing interesting relationships between causal models, measurement noise,
out-of-domain generalization, and reliance on spurious signals. Our analysis
provides some insights that help to explain the efficacy of CAD. Moreover, we
develop the hypothesis that while adding noise to causal features should
degrade both in-domain and out-of-domain performance, adding noise to
non-causal features should lead to relative improvements in out-of-domain
performance. This idea inspires a speculative test for determining whether a
feature attribution technique has identified the causal spans. If adding noise
(e.g., by random word flips) to the highlighted spans degrades both in-domain
and out-of-domain performance on a battery of challenge datasets, but adding
noise to the complement gives improvements out-of-domain, it suggests we have
identified causal spans. We present a large-scale empirical study comparing
spans edited to create CAD to those selected by attention and saliency maps.
Across numerous domains and models, we find that the hypothesized phenomenon is
pronounced for CAD.
| 2,021 | Computation and Language |
Lifelong Language Knowledge Distillation | It is challenging to perform lifelong language learning (LLL) on a stream of
different tasks without any performance degradation comparing to the multi-task
counterparts. To address this issue, we present Lifelong Language Knowledge
Distillation (L2KD), a simple but efficient method that can be easily applied
to existing LLL architectures in order to mitigate the degradation.
Specifically, when the LLL model is trained on a new task, we assign a teacher
model to first learn the new task, and pass the knowledge to the LLL model via
knowledge distillation. Therefore, the LLL model can better adapt to the new
task while keeping the previously learned knowledge. Experiments show that the
proposed L2KD consistently improves previous state-of-the-art models, and the
degradation comparing to multi-task models in LLL tasks is well mitigated for
both sequence generation and text classification tasks.
| 2,020 | Computation and Language |
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet
Lab Protocols using Structured Learning Ensemble and Contextualised
Embeddings | In this paper, we describe the approach that we employed to address the task
of Entity Recognition over Wet Lab Protocols -- a shared task in EMNLP
WNUT-2020 Workshop. Our approach is composed of two phases. In the first phase,
we experiment with various contextualised word embeddings (like Flair,
BERT-based) and a BiLSTM-CRF model to arrive at the best-performing
architecture. In the second phase, we create an ensemble composed of eleven
BiLSTM-CRF models. The individual models are trained on random train-validation
splits of the complete dataset. Here, we also experiment with different output
merging schemes, including Majority Voting and Structured Learning Ensembling
(SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for
the partial and exact match of the entity spans, respectively. We were ranked
first and second, in terms of partial and exact match, respectively.
| 2,020 | Computation and Language |
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