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Limits of Detecting Text Generated by Large-Scale Language Models | Some consider large-scale language models that can generate long and coherent
pieces of text as dangerous, since they may be used in misinformation
campaigns. Here we formulate large-scale language model output detection as a
hypothesis testing problem to classify text as genuine or generated. We show
that error exponents for particular language models are bounded in terms of
their perplexity, a standard measure of language generation performance. Under
the assumption that human language is stationary and ergodic, the formulation
is extended from considering specific language models to considering maximum
likelihood language models, among the class of k-order Markov approximations;
error probabilities are characterized. Some discussion of incorporating
semantic side information is also given.
| 2,020 | Computation and Language |
Multilingual Alignment of Contextual Word Representations | We propose procedures for evaluating and strengthening contextual embedding
alignment and show that they are useful in analyzing and improving multilingual
BERT. In particular, after our proposed alignment procedure, BERT exhibits
significantly improved zero-shot performance on XNLI compared to the base
model, remarkably matching pseudo-fully-supervised translate-train models for
Bulgarian and Greek. Further, to measure the degree of alignment, we introduce
a contextual version of word retrieval and show that it correlates well with
downstream zero-shot transfer. Using this word retrieval task, we also analyze
BERT and find that it exhibits systematic deficiencies, e.g. worse alignment
for open-class parts-of-speech and word pairs written in different scripts,
that are corrected by the alignment procedure. These results support contextual
alignment as a useful concept for understanding large multilingual pre-trained
models.
| 2,020 | Computation and Language |
What Changed Your Mind: The Roles of Dynamic Topics and Discourse in
Argumentation Process | In our world with full of uncertainty, debates and argumentation contribute
to the progress of science and society. Despite of the increasing attention to
characterize human arguments, most progress made so far focus on the debate
outcome, largely ignoring the dynamic patterns in argumentation processes. This
paper presents a study that automatically analyzes the key factors in argument
persuasiveness, beyond simply predicting who will persuade whom. Specifically,
we propose a novel neural model that is able to dynamically track the changes
of latent topics and discourse in argumentative conversations, allowing the
investigation of their roles in influencing the outcomes of persuasion.
Extensive experiments have been conducted on argumentative conversations on
both social media and supreme court. The results show that our model
outperforms state-of-the-art models in identifying persuasive arguments via
explicitly exploring dynamic factors of topic and discourse. We further analyze
the effects of topics and discourse on persuasiveness, and find that they are
both useful - topics provide concrete evidence while superior discourse styles
may bias participants, especially in social media arguments. In addition, we
draw some findings from our empirical results, which will help people better
engage in future persuasive conversations.
| 2,020 | Computation and Language |
A Study of Human Summaries of Scientific Articles | Researchers and students face an explosion of newly published papers which
may be relevant to their work. This led to a trend of sharing human summaries
of scientific papers. We analyze the summaries shared in one of these platforms
Shortscience.org. The goal is to characterize human summaries of scientific
papers, and use some of the insights obtained to improve and adapt existing
automatic summarization systems to the domain of scientific papers.
| 2,020 | Computation and Language |
A Probabilistic Formulation of Unsupervised Text Style Transfer | We present a deep generative model for unsupervised text style transfer that
unifies previously proposed non-generative techniques. Our probabilistic
approach models non-parallel data from two domains as a partially observed
parallel corpus. By hypothesizing a parallel latent sequence that generates
each observed sequence, our model learns to transform sequences from one domain
to another in a completely unsupervised fashion. In contrast with traditional
generative sequence models (e.g. the HMM), our model makes few assumptions
about the data it generates: it uses a recurrent language model as a prior and
an encoder-decoder as a transduction distribution. While computation of
marginal data likelihood is intractable in this model class, we show that
amortized variational inference admits a practical surrogate. Further, by
drawing connections between our variational objective and other recent
unsupervised style transfer and machine translation techniques, we show how our
probabilistic view can unify some known non-generative objectives such as
backtranslation and adversarial loss. Finally, we demonstrate the effectiveness
of our method on a wide range of unsupervised style transfer tasks, including
sentiment transfer, formality transfer, word decipherment, author imitation,
and related language translation. Across all style transfer tasks, our approach
yields substantial gains over state-of-the-art non-generative baselines,
including the state-of-the-art unsupervised machine translation techniques that
our approach generalizes. Further, we conduct experiments on a standard
unsupervised machine translation task and find that our unified approach
matches the current state-of-the-art.
| 2,020 | Computation and Language |
Automatic Discourse Segmentation: an evaluation in French | In this article, we describe some discursive segmentation methods as well as
a preliminary evaluation of the segmentation quality. Although our experiment
were carried for documents in French, we have developed three discursive
segmentation models solely based on resources simultaneously available in
several languages: marker lists and a statistic POS labeling. We have also
carried out automatic evaluations of these systems against the Annodis corpus,
which is a manually annotated reference. The results obtained are very
encouraging.
| 2,020 | Computation and Language |
Training with Streaming Annotation | In this paper, we address a practical scenario where training data is
released in a sequence of small-scale batches and annotation in earlier phases
has lower quality than the later counterparts. To tackle the situation, we
utilize a pre-trained transformer network to preserve and integrate the most
salient document information from the earlier batches while focusing on the
annotation (presumably with higher quality) from the current batch. Using event
extraction as a case study, we demonstrate in the experiments that our proposed
framework can perform better than conventional approaches (the improvement
ranges from 3.6 to 14.9% absolute F-score gain), especially when there is more
noise in the early annotation; and our approach spares 19.1% time with regard
to the best conventional method.
| 2,020 | Computation and Language |
Performance Comparison of Crowdworkers and NLP Tools on Named-Entity
Recognition and Sentiment Analysis of Political Tweets | We report results of a comparison of the accuracy of crowdworkers and seven
Natural Language Processing (NLP) toolkits in solving two important NLP tasks,
named-entity recognition (NER) and entity-level sentiment (ELS) analysis. We
here focus on a challenging dataset, 1,000 political tweets that were collected
during the U.S. presidential primary election in February 2016. Each tweet
refers to at least one of four presidential candidates, i.e., four named
entities. The groundtruth, established by experts in political communication,
has entity-level sentiment information for each candidate mentioned in the
tweet. We tested several commercial and open-source tools. Our experiments show
that, for our dataset of political tweets, the most accurate NER system, Google
Cloud NL, performed almost on par with crowdworkers, but the most accurate ELS
analysis system, TensiStrength, did not match the accuracy of crowdworkers by a
large margin of more than 30 percent points.
| 2,020 | Computation and Language |
Non-Autoregressive Neural Dialogue Generation | Maximum Mutual information (MMI), which models the bidirectional dependency
between responses ($y$) and contexts ($x$), i.e., the forward probability $\log
p(y|x)$ and the backward probability $\log p(x|y)$, has been widely used as the
objective in the \sts model to address the dull-response issue in open-domain
dialog generation. Unfortunately, under the framework of the \sts model, direct
decoding from $\log p(y|x) + \log p(x|y)$ is infeasible since the second part
(i.e., $p(x|y)$) requires the completion of target generation before it can be
computed, and the search space for $y$ is enormous. Empirically, an N-best list
is first generated given $p(y|x)$, and $p(x|y)$ is then used to rerank the
N-best list, which inevitably results in non-globally-optimal solutions. In
this paper, we propose to use non-autoregressive (non-AR) generation model to
address this non-global optimality issue. Since target tokens are generated
independently in non-AR generation, $p(x|y)$ for each target word can be
computed as soon as it's generated, and does not have to wait for the
completion of the whole sequence. This naturally resolves the non-global
optimal issue in decoding. Experimental results demonstrate that the proposed
non-AR strategy produces more diverse, coherent, and appropriate responses,
yielding substantive gains in BLEU scores and in human evaluations.
| 2,020 | Computation and Language |
Learning Coupled Policies for Simultaneous Machine Translation using
Imitation Learning | We present a novel approach to efficiently learn a simultaneous translation
model with coupled programmer-interpreter policies. First, wepresent an
algorithmic oracle to produce oracle READ/WRITE actions for training bilingual
sentence-pairs using the notion of word alignments. This oracle actions are
designed to capture enough information from the partial input before writing
the output. Next, we perform a coupled scheduled sampling to effectively
mitigate the exposure bias when learning both policies jointly with imitation
learning. Experiments on six language-pairs show our method outperforms strong
baselines in terms of translation quality while keeping the translation delay
low.
| 2,021 | Computation and Language |
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning | Recent powerful pre-trained language models have achieved remarkable
performance on most of the popular datasets for reading comprehension. It is
time to introduce more challenging datasets to push the development of this
field towards more comprehensive reasoning of text. In this paper, we introduce
a new Reading Comprehension dataset requiring logical reasoning (ReClor)
extracted from standardized graduate admission examinations. As earlier studies
suggest, human-annotated datasets usually contain biases, which are often
exploited by models to achieve high accuracy without truly understanding the
text. In order to comprehensively evaluate the logical reasoning ability of
models on ReClor, we propose to identify biased data points and separate them
into EASY set while the rest as HARD set. Empirical results show that
state-of-the-art models have an outstanding ability to capture biases contained
in the dataset with high accuracy on EASY set. However, they struggle on HARD
set with poor performance near that of random guess, indicating more research
is needed to essentially enhance the logical reasoning ability of current
models.
| 2,020 | Computation and Language |
The Rumour Mill: Making the Spread of Misinformation Explicit and
Tangible | Misinformation spread presents a technological and social threat to society.
With the advance of AI-based language models, automatically generated texts
have become difficult to identify and easy to create at scale. We present "The
Rumour Mill", a playful art piece, designed as a commentary on the spread of
rumours and automatically-generated misinformation. The mill is a tabletop
interactive machine, which invites a user to experience the process of creating
believable text by interacting with different tangible controls on the mill.
The user manipulates visible parameters to adjust the genre and type of an
automatically generated text rumour. The Rumour Mill is a physical
demonstration of the state of current technology and its ability to generate
and manipulate natural language text, and of the act of starting and spreading
rumours.
| 2,020 | Computation and Language |
Constructing a Highlight Classifier with an Attention Based LSTM Neural
Network | Data is being produced in larger quantities than ever before in human
history. It's only natural to expect a rise in demand for technology that aids
humans in sifting through and analyzing this inexhaustible supply of
information. This need exists in the market research industry, where large
amounts of consumer research data is collected through video recordings. At
present, the standard method for analyzing video data is human labor. Market
researchers manually review the vast majority of consumer research video in
order to identify relevant portions - highlights. The industry state of the art
turnaround ratio is 2.2 - for every hour of video content 2.2 hours of manpower
are required. In this study we present a novel approach for NLP-based highlight
identification and extraction based on a supervised learning model that aides
market researchers in sifting through their data. Our approach hinges on a
manually curated user-generated highlight clips constructed from long and
short-form video data. The problem is best suited for an NLP approach due to
the availability of video transcription. We evaluate multiple classes of
models, from gradient boosting to recurrent neural networks, comparing their
performance in extraction and identification of highlights. The best performing
models are then evaluated using four sampling methods designed to analyze
documents much larger than the maximum input length of the classifiers. We
report very high performances for the standalone classifiers, ROC AUC scores in
the range 0.93-0.94, but observe a significant drop in effectiveness when
evaluated on large documents. Based on our results we suggest combinations of
models/sampling algorithms for various use cases.
| 2,020 | Computation and Language |
Adjusting Image Attributes of Localized Regions with Low-level Dialogue | Natural Language Image Editing (NLIE) aims to use natural language
instructions to edit images. Since novices are inexperienced with image editing
techniques, their instructions are often ambiguous and contain high-level
abstractions that tend to correspond to complex editing steps to accomplish.
Motivated by this inexperience aspect, we aim to smooth the learning curve by
teaching the novices to edit images using low-level commanding terminologies.
Towards this end, we develop a task-oriented dialogue system to investigate
low-level instructions for NLIE. Our system grounds language on the level of
edit operations, and suggests options for a user to choose from. Though
compelled to express in low-level terms, a user evaluation shows that 25% of
users found our system easy-to-use, resonating with our motivation. An analysis
shows that users generally adapt to utilizing the proposed low-level language
interface. In this study, we identify that object segmentation as the key
factor to the user satisfaction. Our work demonstrates the advantages of the
low-level, direct language-action mapping approach that can be applied to other
problem domains beyond image editing such as audio editing or industrial
design.
| 2,020 | Computation and Language |
Two Huge Title and Keyword Generation Corpora of Research Articles | Recent developments in sequence-to-sequence learning with neural networks
have considerably improved the quality of automatically generated text
summaries and document keywords, stipulating the need for even bigger training
corpora. Metadata of research articles are usually easy to find online and can
be used to perform research on various tasks. In this paper, we introduce two
huge datasets for text summarization (OAGSX) and keyword generation (OAGKX)
research, containing 34 million and 23 million records, respectively. The data
were retrieved from the Open Academic Graph which is a network of research
profiles and publications. We carefully processed each record and also tried
several extractive and abstractive methods of both tasks to create performance
baselines for other researchers. We further illustrate the performance of those
methods previewing their outputs. In the near future, we would like to apply
topic modeling on the two sets to derive subsets of research articles from more
specific disciplines.
| 2,020 | Computation and Language |
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and
Diagnosing Dialogue Systems | We present ConvLab-2, an open-source toolkit that enables researchers to
build task-oriented dialogue systems with state-of-the-art models, perform an
end-to-end evaluation, and diagnose the weakness of systems. As the successor
of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab's framework but
integrates more powerful dialogue models and supports more datasets. Besides,
we have developed an analysis tool and an interactive tool to assist
researchers in diagnosing dialogue systems. The analysis tool presents rich
statistics and summarizes common mistakes from simulated dialogues, which
facilitates error analysis and system improvement. The interactive tool
provides a user interface that allows developers to diagnose an assembled
dialogue system by interacting with the system and modifying the output of each
system component.
| 2,020 | Computation and Language |
Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis
and Natural Language Inference | Aspect based sentiment analysis aims to identify the sentimental tendency
towards a given aspect in text. Fine-tuning of pretrained BERT performs
excellent on this task and achieves state-of-the-art performances. Existing
BERT-based works only utilize the last output layer of BERT and ignore the
semantic knowledge in the intermediate layers. This paper explores the
potential of utilizing BERT intermediate layers to enhance the performance of
fine-tuning of BERT. To the best of our knowledge, no existing work has been
done on this research. To show the generality, we also apply this approach to a
natural language inference task. Experimental results demonstrate the
effectiveness and generality of the proposed approach.
| 2,020 | Computation and Language |
Joint Embedding in Named Entity Linking on Sentence Level | Named entity linking is to map an ambiguous mention in documents to an entity
in a knowledge base. The named entity linking is challenging, given the fact
that there are multiple candidate entities for a mention in a document. It is
difficult to link a mention when it appears multiple times in a document, since
there are conflicts by the contexts around the appearances of the mention. In
addition, it is difficult since the given training dataset is small due to the
reason that it is done manually to link a mention to its mapping entity. In the
literature, there are many reported studies among which the recent embedding
methods learn vectors of entities from the training dataset at document level.
To address these issues, we focus on how to link entity for mentions at a
sentence level, which reduces the noises introduced by different appearances of
the same mention in a document at the expense of insufficient information to be
used. We propose a new unified embedding method by maximizing the relationships
learned from knowledge graphs. We confirm the effectiveness of our method in
our experimental studies.
| 2,020 | Computation and Language |
Learning to Compare for Better Training and Evaluation of Open Domain
Natural Language Generation Models | Automated evaluation of open domain natural language generation (NLG) models
remains a challenge and widely used metrics such as BLEU and Perplexity can be
misleading in some cases. In our paper, we propose to evaluate natural language
generation models by learning to compare a pair of generated sentences by
fine-tuning BERT, which has been shown to have good natural language
understanding ability. We also propose to evaluate the model-level quality of
NLG models with sample-level comparison results with skill rating system. While
able to be trained in a fully self-supervised fashion, our model can be further
fine-tuned with a little amount of human preference annotation to better
imitate human judgment. In addition to evaluating trained models, we propose to
apply our model as a performance indicator during training for better
hyperparameter tuning and early-stopping. We evaluate our approach on both
story generation and chit-chat dialogue response generation. Experimental
results show that our model correlates better with human preference compared
with previous automated evaluation approaches. Training with the proposed
metric yields better performance in human evaluation, which further
demonstrates the effectiveness of the proposed model.
| 2,020 | Computation and Language |
Exploiting the Matching Information in the Support Set for Few Shot
Event Classification | The existing event classification (EC) work primarily focuseson the
traditional supervised learning setting in which models are unableto extract
event mentions of new/unseen event types. Few-shot learninghas not been
investigated in this area although it enables EC models toextend their
operation to unobserved event types. To fill in this gap, inthis work, we
investigate event classification under the few-shot learningsetting. We propose
a novel training method for this problem that exten-sively exploit the support
set during the training process of a few-shotlearning model. In particular, in
addition to matching the query exam-ple with those in the support set for
training, we seek to further matchthe examples within the support set
themselves. This method providesmore training signals for the models and can be
applied to every metric-learning-based few-shot learning methods. Our extensive
experiments ontwo benchmark EC datasets show that the proposed method can
improvethe best reported few-shot learning models by up to 10% on accuracyfor
event classification
| 2,020 | Computation and Language |
Keyphrase Extraction with Span-based Feature Representations | Keyphrases are capable of providing semantic metadata characterizing
documents and producing an overview of the content of a document. Since
keyphrase extraction is able to facilitate the management, categorization, and
retrieval of information, it has received much attention in recent years. There
are three approaches to address keyphrase extraction: (i) traditional two-step
ranking method, (ii) sequence labeling and (iii) generation using neural
networks. Two-step ranking approach is based on feature engineering, which is
labor intensive and domain dependent. Sequence labeling is not able to tackle
overlapping phrases. Generation methods (i.e., Sequence-to-sequence neural
network models) overcome those shortcomings, so they have been widely studied
and gain state-of-the-art performance. However, generation methods can not
utilize context information effectively. In this paper, we propose a novelty
Span Keyphrase Extraction model that extracts span-based feature representation
of keyphrase directly from all the content tokens. In this way, our model
obtains representation for each keyphrase and further learns to capture the
interaction between keyphrases in one document to get better ranking results.
In addition, with the help of tokens, our model is able to extract overlapped
keyphrases. Experimental results on the benchmark datasets show that our
proposed model outperforms the existing methods by a large margin.
| 2,020 | Computation and Language |
Comparison of Turkish Word Representations Trained on Different
Morphological Forms | Increased popularity of different text representations has also brought many
improvements in Natural Language Processing (NLP) tasks. Without need of
supervised data, embeddings trained on large corpora provide us meaningful
relations to be used on different NLP tasks. Even though training these vectors
is relatively easy with recent methods, information gained from the data
heavily depends on the structure of the corpus language. Since the popularly
researched languages have a similar morphological structure, problems occurring
for morphologically rich languages are mainly disregarded in studies. For
morphologically rich languages, context-free word vectors ignore morphological
structure of languages. In this study, we prepared texts in morphologically
different forms in a morphologically rich language, Turkish, and compared the
results on different intrinsic and extrinsic tasks. To see the effect of
morphological structure, we trained word2vec model on texts which lemma and
suffixes are treated differently. We also trained subword model fastText and
compared the embeddings on word analogy, text classification, sentimental
analysis, and language model tasks.
| 2,020 | Computation and Language |
Unsupervised Separation of Native and Loanwords for Malayalam and Telugu | Quite often, words from one language are adopted within a different language
without translation; these words appear in transliterated form in text written
in the latter language. This phenomenon is particularly widespread within
Indian languages where many words are loaned from English. In this paper, we
address the task of identifying loanwords automatically and in an unsupervised
manner, from large datasets of words from agglutinative Dravidian languages. We
target two specific languages from the Dravidian family, viz., Malayalam and
Telugu. Based on familiarity with the languages, we outline an observation that
native words in both these languages tend to be characterized by a much more
versatile stem - stem being a shorthand to denote the subword sequence formed
by the first few characters of the word - than words that are loaned from other
languages. We harness this observation to build an objective function and an
iterative optimization formulation to optimize for it, yielding a scoring of
each word's nativeness in the process. Through an extensive empirical analysis
over real-world datasets from both Malayalam and Telugu, we illustrate the
effectiveness of our method in quantifying nativeness effectively over
available baselines for the task.
| 2,020 | Computation and Language |
Sparse and Structured Visual Attention | Visual attention mechanisms are widely used in multimodal tasks, as visual
question answering (VQA). One drawback of softmax-based attention mechanisms is
that they assign some probability mass to all image regions, regardless of
their adjacency structure and of their relevance to the text. In this paper, to
better link the image structure with the text, we replace the traditional
softmax attention mechanism with two alternative sparsity-promoting
transformations: sparsemax, which is able to select only the relevant regions
(assigning zero weight to the rest), and a newly proposed Total-Variation
Sparse Attention (TVmax), which further encourages the joint selection of
adjacent spatial locations. Experiments in VQA show gains in accuracy as well
as higher similarity to human attention, which suggests better
interpretability.
| 2,021 | Computation and Language |
Sentiment Analysis Using Averaged Weighted Word Vector Features | People use the world wide web heavily to share their experience with entities
such as products, services, or travel destinations. Texts that provide online
feedback in the form of reviews and comments are essential to make consumer
decisions. These comments create a valuable source that may be used to measure
satisfaction related to products or services. Sentiment analysis is the task of
identifying opinions expressed in such text fragments. In this work, we develop
two methods that combine different types of word vectors to learn and estimate
polarity of reviews. We develop average review vectors from word vectors and
add weights to this review vectors using word frequencies in positive and
negative sensitivity-tagged reviews. We applied the methods to several datasets
from different domains that are used as standard benchmarks for sentiment
analysis. We ensemble the techniques with each other and existing methods, and
we make a comparison with the approaches in the literature. The results show
that the performances of our approaches outperform the state-of-the-art success
rates.
| 2,023 | Computation and Language |
Pre-Training for Query Rewriting in A Spoken Language Understanding
System | Query rewriting (QR) is an increasingly important technique to reduce
customer friction caused by errors in a spoken language understanding pipeline,
where the errors originate from various sources such as speech recognition
errors, language understanding errors or entity resolution errors. In this
work, we first propose a neural-retrieval based approach for query rewriting.
Then, inspired by the wide success of pre-trained contextual language
embeddings, and also as a way to compensate for insufficient QR training data,
we propose a language-modeling (LM) based approach to pre-train query
embeddings on historical user conversation data with a voice assistant. In
addition, we propose to use the NLU hypotheses generated by the language
understanding system to augment the pre-training. Our experiments show
pre-training provides rich prior information and help the QR task achieve
strong performance. We also show joint pre-training with NLU hypotheses has
further benefit. Finally, after pre-training, we find a small set of rewrite
pairs is enough to fine-tune the QR model to outperform a strong baseline by
full training on all QR training data.
| 2,020 | Computation and Language |
Looking Enhances Listening: Recovering Missing Speech Using Images | Speech is understood better by using visual context; for this reason, there
have been many attempts to use images to adapt automatic speech recognition
(ASR) systems. Current work, however, has shown that visually adapted ASR
models only use images as a regularization signal, while completely ignoring
their semantic content. In this paper, we present a set of experiments where we
show the utility of the visual modality under noisy conditions. Our results
show that multimodal ASR models can recover words which are masked in the input
acoustic signal, by grounding its transcriptions using the visual
representations. We observe that integrating visual context can result in up to
35% relative improvement in masked word recovery. These results demonstrate
that end-to-end multimodal ASR systems can become more robust to noise by
leveraging the visual context.
| 2,020 | Computation and Language |
HULK: An Energy Efficiency Benchmark Platform for Responsible Natural
Language Processing | Computation-intensive pretrained models have been taking the lead of many
natural language processing benchmarks such as GLUE. However, energy efficiency
in the process of model training and inference becomes a critical bottleneck.
We introduce HULK, a multi-task energy efficiency benchmarking platform for
responsible natural language processing. With HULK, we compare pretrained
models' energy efficiency from the perspectives of time and cost. Baseline
benchmarking results are provided for further analysis. The fine-tuning
efficiency of different pretrained models can differ a lot among different
tasks and fewer parameter number does not necessarily imply better efficiency.
We analyzed such phenomenon and demonstrate the method of comparing the
multi-task efficiency of pretrained models. Our platform is available at
https://sites.engineering.ucsb.edu/~xiyou/hulk/.
| 2,020 | Computation and Language |
Transformers as Soft Reasoners over Language | Beginning with McCarthy's Advice Taker (1959), AI has pursued the goal of
providing a system with explicit, general knowledge and having the system
reason over that knowledge. However, expressing the knowledge in a formal
(logical or probabilistic) representation has been a major obstacle to this
research. This paper investigates a modern approach to this problem where the
facts and rules are provided as natural language sentences, thus bypassing a
formal representation. We train transformers to reason (or emulate reasoning)
over these sentences using synthetically generated data. Our models, that we
call RuleTakers, provide the first empirical demonstration that this kind of
soft reasoning over language is learnable, can achieve high (99%) accuracy, and
generalizes to test data requiring substantially deeper chaining than seen
during training (95%+ scores). We also demonstrate that the models transfer
well to two hand-authored rulebases, and to rulebases paraphrased into more
natural language. These findings are significant as it suggests a new role for
transformers, namely as limited "soft theorem provers" operating over explicit
theories in language. This in turn suggests new possibilities for
explainability, correctability, and counterfactual reasoning in
question-answering.
| 2,020 | Computation and Language |
Understanding patient complaint characteristics using contextual
clinical BERT embeddings | In clinical conversational applications, extracted entities tend to capture
the main subject of a patient's complaint, namely symptoms or diseases.
However, they mostly fail to recognize the characterizations of a complaint
such as the time, the onset, and the severity. For example, if the input is "I
have a headache and it is extreme", state-of-the-art models only recognize the
main symptom entity - headache, but ignore the severity factor of "extreme",
that characterizes headache. In this paper, we design a two-stage approach to
detect the characterizations of entities like symptoms presented by general
users in contexts where they would describe their symptoms to a clinician. We
use Word2Vec and BERT to encode clinical text given by the patients. We
transform the output and re-frame the task as multi-label classification
problem. Finally, we combine the processed encodings with the Linear
Discriminant Analysis (LDA) algorithm to classify the characterizations of the
main entity. Experimental results demonstrate that our method achieves 40-50%
improvement on the accuracy over the state-of-the-art models.
| 2,020 | Computation and Language |
Zero-Resource Cross-Domain Named Entity Recognition | Existing models for cross-domain named entity recognition (NER) rely on
numerous unlabeled corpus or labeled NER training data in target domains.
However, collecting data for low-resource target domains is not only expensive
but also time-consuming. Hence, we propose a cross-domain NER model that does
not use any external resources. We first introduce a Multi-Task Learning (MTL)
by adding a new objective function to detect whether tokens are named entities
or not. We then introduce a framework called Mixture of Entity Experts (MoEE)
to improve the robustness for zero-resource domain adaptation. Finally,
experimental results show that our model outperforms strong unsupervised
cross-domain sequence labeling models, and the performance of our model is
close to that of the state-of-the-art model which leverages extensive
resources.
| 2,020 | Computation and Language |
A Data Efficient End-To-End Spoken Language Understanding Architecture | End-to-end architectures have been recently proposed for spoken language
understanding (SLU) and semantic parsing. Based on a large amount of data,
those models learn jointly acoustic and linguistic-sequential features. Such
architectures give very good results in the context of domain, intent and slot
detection, their application in a more complex semantic chunking and tagging
task is less easy. For that, in many cases, models are combined with an
external language model to enhance their performance.
In this paper we introduce a data efficient system which is trained
end-to-end, with no additional, pre-trained external module. One key feature of
our approach is an incremental training procedure where acoustic, language and
semantic models are trained sequentially one after the other. The proposed
model has a reasonable size and achieves competitive results with respect to
state-of-the-art while using a small training dataset. In particular, we reach
24.02% Concept Error Rate (CER) on MEDIA/test while training on MEDIA/train
without any additional data.
| 2,020 | Computation and Language |
Integrating Discrete and Neural Features via Mixed-feature
Trans-dimensional Random Field Language Models | There has been a long recognition that discrete features (n-gram features)
and neural network based features have complementary strengths for language
models (LMs). Improved performance can be obtained by model interpolation,
which is, however, a suboptimal two-step integration of discrete and neural
features. The trans-dimensional random field (TRF) framework has the potential
advantage of being able to flexibly integrate a richer set of features.
However, either discrete or neural features are used alone in previous TRF LMs.
This paper develops a mixed-feature TRF LM and demonstrates its advantage in
integrating discrete and neural features. Various LMs are trained over PTB and
Google one-billion-word datasets, and evaluated in N-best list rescoring
experiments for speech recognition. Among all single LMs (i.e. without model
interpolation), the mixed-feature TRF LMs perform the best, improving over both
discrete TRF LMs and neural TRF LMs alone, and also being significantly better
than LSTM LMs. Compared to interpolating two separately trained models with
discrete and neural features respectively, the performance of mixed-feature TRF
LMs matches the best interpolated model, and with simplified one-step training
process and reduced training time.
| 2,020 | Computation and Language |
Dialogue history integration into end-to-end signal-to-concept spoken
language understanding systems | This work investigates the embeddings for representing dialog history in
spoken language understanding (SLU) systems. We focus on the scenario when the
semantic information is extracted directly from the speech signal by means of a
single end-to-end neural network model. We proposed to integrate dialogue
history into an end-to-end signal-to-concept SLU system. The dialog history is
represented in the form of dialog history embedding vectors (so-called
h-vectors) and is provided as an additional information to end-to-end SLU
models in order to improve the system performance. Three following types of
h-vectors are proposed and experimentally evaluated in this paper: (1)
supervised-all embeddings predicting bag-of-concepts expected in the answer of
the user from the last dialog system response; (2) supervised-freq embeddings
focusing on predicting only a selected set of semantic concept (corresponding
to the most frequent errors in our experiments); and (3) unsupervised
embeddings. Experiments on the MEDIA corpus for the semantic slot filling task
demonstrate that the proposed h-vectors improve the model performance.
| 2,020 | Computation and Language |
FQuAD: French Question Answering Dataset | Recent advances in the field of language modeling have improved
state-of-the-art results on many Natural Language Processing tasks. Among them,
Reading Comprehension has made significant progress over the past few years.
However, most results are reported in English since labeled resources available
in other languages, such as French, remain scarce. In the present work, we
introduce the French Question Answering Dataset (FQuAD). FQuAD is a French
Native Reading Comprehension dataset of questions and answers on a set of
Wikipedia articles that consists of 25,000+ samples for the 1.0 version and
60,000+ samples for the 1.1 version. We train a baseline model which achieves
an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In order
to track the progress of French Question Answering models we propose a
leader-board and we have made the 1.0 version of our dataset freely available
at https://illuin-tech.github.io/FQuAD-explorer/.
| 2,020 | Computation and Language |
Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base | We describe a novel way of representing a symbolic knowledge base (KB) called
a sparse-matrix reified KB. This representation enables neural modules that are
fully differentiable, faithful to the original semantics of the KB, expressive
enough to model multi-hop inferences, and scalable enough to use with
realistically large KBs. The sparse-matrix reified KB can be distributed across
multiple GPUs, can scale to tens of millions of entities and facts, and is
orders of magnitude faster than naive sparse-matrix implementations. The
reified KB enables very simple end-to-end architectures to obtain competitive
performance on several benchmarks representing two families of tasks: KB
completion, and learning semantic parsers from denotations.
| 2,020 | Computation and Language |
Transformer on a Diet | Transformer has been widely used thanks to its ability to capture sequence
information in an efficient way. However, recent developments, such as BERT and
GPT-2, deliver only heavy architectures with a focus on effectiveness. In this
paper, we explore three carefully-designed light Transformer architectures to
figure out whether the Transformer with less computations could produce
competitive results. Experimental results on language model benchmark datasets
hint that such trade-off is promising, and the light Transformer reduces 70%
parameters at best, while obtains competitive perplexity compared to standard
Transformer. The source code is publicly available.
| 2,020 | Computation and Language |
Semantic Relatedness and Taxonomic Word Embeddings | This paper connects a series of papers dealing with taxonomic word
embeddings. It begins by noting that there are different types of semantic
relatedness and that different lexical representations encode different forms
of relatedness. A particularly important distinction within semantic
relatedness is that of thematic versus taxonomic relatedness. Next, we present
a number of experiments that analyse taxonomic embeddings that have been
trained on a synthetic corpus that has been generated via a random walk over a
taxonomy. These experiments demonstrate how the properties of the synthetic
corpus, such as the percentage of rare words, are affected by the shape of the
knowledge graph the corpus is generated from. Finally, we explore the
interactions between the relative sizes of natural and synthetic corpora on the
performance of embeddings when taxonomic and thematic embeddings are combined.
| 2,020 | Computation and Language |
Fine-Tuning Pretrained Language Models: Weight Initializations, Data
Orders, and Early Stopping | Fine-tuning pretrained contextual word embedding models to supervised
downstream tasks has become commonplace in natural language processing. This
process, however, is often brittle: even with the same hyperparameter values,
distinct random seeds can lead to substantially different results. To better
understand this phenomenon, we experiment with four datasets from the GLUE
benchmark, fine-tuning BERT hundreds of times on each while varying only the
random seeds. We find substantial performance increases compared to previously
reported results, and we quantify how the performance of the best-found model
varies as a function of the number of fine-tuning trials. Further, we examine
two factors influenced by the choice of random seed: weight initialization and
training data order. We find that both contribute comparably to the variance of
out-of-sample performance, and that some weight initializations perform well
across all tasks explored. On small datasets, we observe that many fine-tuning
trials diverge part of the way through training, and we offer best practices
for practitioners to stop training less promising runs early. We publicly
release all of our experimental data, including training and validation scores
for 2,100 trials, to encourage further analysis of training dynamics during
fine-tuning.
| 2,020 | Computation and Language |
Deeper Task-Specificity Improves Joint Entity and Relation Extraction | Multi-task learning (MTL) is an effective method for learning related tasks,
but designing MTL models necessitates deciding which and how many parameters
should be task-specific, as opposed to shared between tasks. We investigate
this issue for the problem of jointly learning named entity recognition (NER)
and relation extraction (RE) and propose a novel neural architecture that
allows for deeper task-specificity than does prior work. In particular, we
introduce additional task-specific bidirectional RNN layers for both the NER
and RE tasks and tune the number of shared and task-specific layers separately
for different datasets. We achieve state-of-the-art (SOTA) results for both
tasks on the ADE dataset; on the CoNLL04 dataset, we achieve SOTA results on
the NER task and competitive results on the RE task while using an order of
magnitude fewer trainable parameters than the current SOTA architecture. An
ablation study confirms the importance of the additional task-specific layers
for achieving these results. Our work suggests that previous solutions to joint
NER and RE undervalue task-specificity and demonstrates the importance of
correctly balancing the number of shared and task-specific parameters for MTL
approaches in general.
| 2,020 | Computation and Language |
Supervised Phrase-boundary Embeddings | We propose a new word embedding model, called SPhrase, that incorporates
supervised phrase information. Our method modifies traditional word embeddings
by ensuring that all target words in a phrase have exactly the same context. We
demonstrate that including this information within a context window produces
superior embeddings for both intrinsic evaluation tasks and downstream
extrinsic tasks.
| 2,020 | Computation and Language |
A Multimodal Dialogue System for Conversational Image Editing | In this paper, we present a multimodal dialogue system for Conversational
Image Editing. We formulate our multimodal dialogue system as a Partially
Observed Markov Decision Process (POMDP) and trained it with Deep Q-Network
(DQN) and a user simulator. Our evaluation shows that the DQN policy
outperforms a rule-based baseline policy, achieving 90\% success rate under
high error rates. We also conducted a real user study and analyzed real user
behavior.
| 2,020 | Computation and Language |
Learning to Generate Multiple Style Transfer Outputs for an Input
Sentence | Text style transfer refers to the task of rephrasing a given text in a
different style. While various methods have been proposed to advance the state
of the art, they often assume the transfer output follows a delta distribution,
and thus their models cannot generate different style transfer results for a
given input text. To address the limitation, we propose a one-to-many text
style transfer framework. In contrast to prior works that learn a one-to-one
mapping that converts an input sentence to one output sentence, our approach
learns a one-to-many mapping that can convert an input sentence to multiple
different output sentences, while preserving the input content. This is
achieved by applying adversarial training with a latent decomposition scheme.
Specifically, we decompose the latent representation of the input sentence to a
style code that captures the language style variation and a content code that
encodes the language style-independent content. We then combine the content
code with the style code for generating a style transfer output. By combining
the same content code with a different style code, we generate a different
style transfer output. Extensive experimental results with comparisons to
several text style transfer approaches on multiple public datasets using a
diverse set of performance metrics validate effectiveness of the proposed
approach.
| 2,020 | Computation and Language |
Exploring Neural Models for Parsing Natural Language into First-Order
Logic | Semantic parsing is the task of obtaining machine-interpretable
representations from natural language text. We consider one such formal
representation - First-Order Logic (FOL) and explore the capability of neural
models in parsing English sentences to FOL. We model FOL parsing as a sequence
to sequence mapping task where given a natural language sentence, it is encoded
into an intermediate representation using an LSTM followed by a decoder which
sequentially generates the predicates in the corresponding FOL formula. We
improve the standard encoder-decoder model by introducing a variable alignment
mechanism that enables it to align variables across predicates in the predicted
FOL. We further show the effectiveness of predicting the category of FOL entity
- Unary, Binary, Variables and Scoped Entities, at each decoder step as an
auxiliary task on improving the consistency of generated FOL. We perform
rigorous evaluations and extensive ablations. We also aim to release our code
as well as large scale FOL dataset along with models to aid further research in
logic-based parsing and inference in NLP.
| 2,020 | Computation and Language |
Neural Machine Translation with Joint Representation | Though early successes of Statistical Machine Translation (SMT) systems are
attributed in part to the explicit modelling of the interaction between any two
source and target units, e.g., alignment, the recent Neural Machine Translation
(NMT) systems resort to the attention which partially encodes the interaction
for efficiency. In this paper, we employ Joint Representation that fully
accounts for each possible interaction. We sidestep the inefficiency issue by
refining representations with the proposed efficient attention operation. The
resulting Reformer models offer a new Sequence-to- Sequence modelling paradigm
besides the Encoder-Decoder framework and outperform the Transformer baseline
in either the small scale IWSLT14 German-English, English-German and IWSLT15
Vietnamese-English or the large scale NIST12 Chinese-English translation tasks
by about 1 BLEU point.We also propose a systematic model scaling approach,
allowing the Reformer model to beat the state-of-the-art Transformer in IWSLT14
German-English and NIST12 Chinese-English with about 50% fewer parameters. The
code is publicly available at https://github.com/lyy1994/reformer.
| 2,020 | Computation and Language |
Towards Detection of Subjective Bias using Contextualized Word
Embeddings | Subjective bias detection is critical for applications like propaganda
detection, content recommendation, sentiment analysis, and bias neutralization.
This bias is introduced in natural language via inflammatory words and phrases,
casting doubt over facts, and presupposing the truth. In this work, we perform
comprehensive experiments for detecting subjective bias using BERT-based models
on the Wiki Neutrality Corpus(WNC). The dataset consists of $360k$ labeled
instances, from Wikipedia edits that remove various instances of the bias. We
further propose BERT-based ensembles that outperform state-of-the-art methods
like $BERT_{large}$ by a margin of $5.6$ F1 score.
| 2,020 | Computation and Language |
SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word
Models | Sentence embedding is an important research topic in natural language
processing (NLP) since it can transfer knowledge to downstream tasks.
Meanwhile, a contextualized word representation, called BERT, achieves the
state-of-the-art performance in quite a few NLP tasks. Yet, it is an open
problem to generate a high quality sentence representation from BERT-based word
models. It was shown in previous study that different layers of BERT capture
different linguistic properties. This allows us to fusion information across
layers to find better sentence representation. In this work, we study the
layer-wise pattern of the word representation of deep contextualized models.
Then, we propose a new sentence embedding method by dissecting BERT-based word
models through geometric analysis of the space spanned by the word
representation. It is called the SBERT-WK method. No further training is
required in SBERT-WK. We evaluate SBERT-WK on semantic textual similarity and
downstream supervised tasks. Furthermore, ten sentence-level probing tasks are
presented for detailed linguistic analysis. Experiments show that SBERT-WK
achieves the state-of-the-art performance. Our codes are publicly available.
| 2,020 | Computation and Language |
The Utility of General Domain Transfer Learning for Medical Language
Tasks | The purpose of this study is to analyze the efficacy of transfer learning
techniques and transformer-based models as applied to medical natural language
processing (NLP) tasks, specifically radiological text classification. We used
1,977 labeled head CT reports, from a corpus of 96,303 total reports, to
evaluate the efficacy of pretraining using general domain corpora and a
combined general and medical domain corpus with a bidirectional representations
from transformers (BERT) model for the purpose of radiological text
classification. Model performance was benchmarked to a logistic regression
using bag-of-words vectorization and a long short-term memory (LSTM)
multi-label multi-class classification model, and compared to the published
literature in medical text classification. The BERT models using either set of
pretrained checkpoints outperformed the logistic regression model, achieving
sample-weighted average F1-scores of 0.87 and 0.87 for the general domain model
and the combined general and biomedical-domain model. General text transfer
learning may be a viable technique to generate state-of-the-art results within
medical NLP tasks on radiological corpora, outperforming other deep models such
as LSTMs. The efficacy of pretraining and transformer-based models could serve
to facilitate the creation of groundbreaking NLP models in the uniquely
challenging data environment of medical text.
| 2,020 | Computation and Language |
Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for
Ainu Language | Ainu is an unwritten language that has been spoken by Ainu people who are one
of the ethnic groups in Japan. It is recognized as critically endangered by
UNESCO and archiving and documentation of its language heritage is of paramount
importance. Although a considerable amount of voice recordings of Ainu folklore
has been produced and accumulated to save their culture, only a quite limited
parts of them are transcribed so far. Thus, we started a project of automatic
speech recognition (ASR) for the Ainu language in order to contribute to the
development of annotated language archives. In this paper, we report speech
corpus development and the structure and performance of end-to-end ASR for
Ainu. We investigated four modeling units (phone, syllable, word piece, and
word) and found that the syllable-based model performed best in terms of both
word and phone recognition accuracy, which were about 60% and over 85%
respectively in speaker-open condition. Furthermore, word and phone accuracy of
80% and 90% has been achieved in a speaker-closed setting. We also found out
that a multilingual ASR training with additional speech corpora of English and
Japanese further improves the speaker-open test accuracy.
| 2,020 | Computation and Language |
Gaussian Smoothen Semantic Features (GSSF) -- Exploring the Linguistic
Aspects of Visual Captioning in Indian Languages (Bengali) Using MSCOCO
Framework | In this work, we have introduced Gaussian Smoothen Semantic Features (GSSF)
for Better Semantic Selection for Indian regional language-based image
captioning and introduced a procedure where we used the existing translation
and English crowd-sourced sentences for training. We have shown that this
architecture is a promising alternative source, where there is a crunch in
resources. Our main contribution of this work is the development of deep
learning architectures for the Bengali language (is the fifth widely spoken
language in the world) with a completely different grammar and language
attributes. We have shown that these are working well for complex applications
like language generation from image contexts and can diversify the
representation through introducing constraints, more extensive features, and
unique feature spaces. We also established that we could achieve absolute
precision and diversity when we use smoothened semantic tensor with the
traditional LSTM and feature decomposition networks. With better learning
architecture, we succeeded in establishing an automated algorithm and
assessment procedure that can help in the evaluation of competent applications
without the requirement for expertise and human intervention.
| 2,020 | Computation and Language |
Multi-layer Representation Fusion for Neural Machine Translation | Neural machine translation systems require a number of stacked layers for
deep models. But the prediction depends on the sentence representation of the
top-most layer with no access to low-level representations. This makes it more
difficult to train the model and poses a risk of information loss to
prediction. In this paper, we propose a multi-layer representation fusion
(MLRF) approach to fusing stacked layers. In particular, we design three fusion
functions to learn a better representation from the stack. Experimental results
show that our approach yields improvements of 0.92 and 0.56 BLEU points over
the strong Transformer baseline on IWSLT German-English and NIST
Chinese-English MT tasks respectively. The result is new state-of-the-art in
German-English translation.
| 2,020 | Computation and Language |
Incorporating BERT into Neural Machine Translation | The recently proposed BERT has shown great power on a variety of natural
language understanding tasks, such as text classification, reading
comprehension, etc. However, how to effectively apply BERT to neural machine
translation (NMT) lacks enough exploration. While BERT is more commonly used as
fine-tuning instead of contextual embedding for downstream language
understanding tasks, in NMT, our preliminary exploration of using BERT as
contextual embedding is better than using for fine-tuning. This motivates us to
think how to better leverage BERT for NMT along this direction. We propose a
new algorithm named BERT-fused model, in which we first use BERT to extract
representations for an input sequence, and then the representations are fused
with each layer of the encoder and decoder of the NMT model through attention
mechanisms. We conduct experiments on supervised (including sentence-level and
document-level translations), semi-supervised and unsupervised machine
translation, and achieve state-of-the-art results on seven benchmark datasets.
Our code is available at \url{https://github.com/bert-nmt/bert-nmt}.
| 2,020 | Computation and Language |
GameWikiSum: a Novel Large Multi-Document Summarization Dataset | Today's research progress in the field of multi-document summarization is
obstructed by the small number of available datasets. Since the acquisition of
reference summaries is costly, existing datasets contain only hundreds of
samples at most, resulting in heavy reliance on hand-crafted features or
necessitating additional, manually annotated data. The lack of large corpora
therefore hinders the development of sophisticated models. Additionally, most
publicly available multi-document summarization corpora are in the news domain,
and no analogous dataset exists in the video game domain. In this paper, we
propose GameWikiSum, a new domain-specific dataset for multi-document
summarization, which is one hundred times larger than commonly used datasets,
and in another domain than news. Input documents consist of long professional
video game reviews as well as references of their gameplay sections in
Wikipedia pages. We analyze the proposed dataset and show that both abstractive
and extractive models can be trained on it. We release GameWikiSum for further
research: https://github.com/Diego999/GameWikiSum.
| 2,020 | Computation and Language |
From English To Foreign Languages: Transferring Pre-trained Language
Models | Pre-trained models have demonstrated their effectiveness in many downstream
natural language processing (NLP) tasks. The availability of multilingual
pre-trained models enables zero-shot transfer of NLP tasks from high resource
languages to low resource ones. However, recent research in improving
pre-trained models focuses heavily on English. While it is possible to train
the latest neural architectures for other languages from scratch, it is
undesirable due to the required amount of compute. In this work, we tackle the
problem of transferring an existing pre-trained model from English to other
languages under a limited computational budget. With a single GPU, our approach
can obtain a foreign BERT base model within a day and a foreign BERT large
within two days. Furthermore, evaluating our models on six languages, we
demonstrate that our models are better than multilingual BERT on two zero-shot
tasks: natural language inference and dependency parsing.
| 2,020 | Computation and Language |
Conditional Self-Attention for Query-based Summarization | Self-attention mechanisms have achieved great success on a variety of NLP
tasks due to its flexibility of capturing dependency between arbitrary
positions in a sequence. For problems such as query-based summarization (Qsumm)
and knowledge graph reasoning where each input sequence is associated with an
extra query, explicitly modeling such conditional contextual dependencies can
lead to a more accurate solution, which however cannot be captured by existing
self-attention mechanisms. In this paper, we propose \textit{conditional
self-attention} (CSA), a neural network module designed for conditional
dependency modeling. CSA works by adjusting the pairwise attention between
input tokens in a self-attention module with the matching score of the inputs
to the given query. Thereby, the contextual dependencies modeled by CSA will be
highly relevant to the query. We further studied variants of CSA defined by
different types of attention. Experiments on Debatepedia and HotpotQA benchmark
datasets show CSA consistently outperforms vanilla Transformer and previous
models for the Qsumm problem.
| 2,020 | Computation and Language |
Annotating and Extracting Synthesis Process of All-Solid-State Batteries
from Scientific Literature | The synthesis process is essential for achieving computational experiment
design in the field of inorganic materials chemistry. In this work, we present
a novel corpus of the synthesis process for all-solid-state batteries and an
automated machine reading system for extracting the synthesis processes buried
in the scientific literature. We define the representation of the synthesis
processes using flow graphs, and create a corpus from the experimental sections
of 243 papers. The automated machine-reading system is developed by a deep
learning-based sequence tagger and simple heuristic rule-based relation
extractor. Our experimental results demonstrate that the sequence tagger with
the optimal setting can detect the entities with a macro-averaged F1 score of
0.826, while the rule-based relation extractor can achieve high performance
with a macro-averaged F1 score of 0.887.
| 2,020 | Computation and Language |
Improving Multi-Turn Response Selection Models with Complementary
Last-Utterance Selection by Instance Weighting | Open-domain retrieval-based dialogue systems require a considerable amount of
training data to learn their parameters. However, in practice, the negative
samples of training data are usually selected from an unannotated conversation
data set at random. The generated training data is likely to contain noise and
affect the performance of the response selection models. To address this
difficulty, we consider utilizing the underlying correlation in the data
resource itself to derive different kinds of supervision signals and reduce the
influence of noisy data. More specially, we consider a main-complementary task
pair. The main task (\ie our focus) selects the correct response given the last
utterance and context, and the complementary task selects the last utterance
given the response and context. The key point is that the output of the
complementary task is used to set instance weights for the main task. We
conduct extensive experiments in two public datasets and obtain significant
improvement in both datasets. We also investigate the variant of our approach
in multiple aspects, and the results have verified the effectiveness of our
approach.
| 2,020 | Computation and Language |
A New Clustering neural network for Chinese word segmentation | In this article I proposed a new model to achieve Chinese word
segmentation(CWS),which may have the potentiality to apply in other domains in
the future.It is a new thinking in CWS compared to previous works,to consider
it as a clustering problem instead of a labeling problem.In this model,LSTM and
self attention structures are used to collect context also sentence level
features in every layer,and after several layers,a clustering model is applied
to split characters into groups,which are the final segmentation results.I call
this model CLNN.This algorithm can reach 98 percent of F score (without OOV
words) and 85 percent to 95 percent F score (with OOV words) in training data
sets.Error analyses shows that OOV words will greatly reduce performances,which
needs a deeper research in the future.
| 2,020 | Computation and Language |
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue | Knowledge-grounded dialogue is a task of generating an informative response
based on both discourse context and external knowledge. As we focus on better
modeling the knowledge selection in the multi-turn knowledge-grounded dialogue,
we propose a sequential latent variable model as the first approach to this
matter. The model named sequential knowledge transformer (SKT) can keep track
of the prior and posterior distribution over knowledge; as a result, it can not
only reduce the ambiguity caused from the diversity in knowledge selection of
conversation but also better leverage the response information for proper
choice of knowledge. Our experimental results show that the proposed model
improves the knowledge selection accuracy and subsequently the performance of
utterance generation. We achieve the new state-of-the-art performance on Wizard
of Wikipedia (Dinan et al., 2019) as one of the most large-scale and
challenging benchmarks. We further validate the effectiveness of our model over
existing conversation methods in another knowledge-based dialogue Holl-E
dataset (Moghe et al., 2018).
| 2,020 | Computation and Language |
A Survey of Deep Learning Techniques for Neural Machine Translation | In recent years, natural language processing (NLP) has got great development
with deep learning techniques. In the sub-field of machine translation, a new
approach named Neural Machine Translation (NMT) has emerged and got massive
attention from both academia and industry. However, with a significant number
of researches proposed in the past several years, there is little work in
investigating the development process of this new technology trend. This
literature survey traces back the origin and principal development timeline of
NMT, investigates the important branches, categorizes different research
orientations, and discusses some future research trends in this field.
| 2,020 | Computation and Language |
Hierarchical Transformer Network for Utterance-level Emotion Recognition | While there have been significant advances in de-tecting emotions in text, in
the field of utter-ance-level emotion recognition (ULER), there are still many
problems to be solved. In this paper, we address some challenges in ULER in
dialog sys-tems. (1) The same utterance can deliver different emotions when it
is in different contexts or from different speakers. (2) Long-range contextual
in-formation is hard to effectively capture. (3) Unlike the traditional text
classification problem, this task is supported by a limited number of datasets,
among which most contain inadequate conversa-tions or speech. To address these
problems, we propose a hierarchical transformer framework (apart from the
description of other studies, the "transformer" in this paper usually refers to
the encoder part of the transformer) with a lower-level transformer to model
the word-level input and an upper-level transformer to capture the context of
utterance-level embeddings. We use a pretrained language model bidirectional
encoder representa-tions from transformers (BERT) as the lower-level
transformer, which is equivalent to introducing external data into the model
and solve the problem of data shortage to some extent. In addition, we add
speaker embeddings to the model for the first time, which enables our model to
capture the in-teraction between speakers. Experiments on three dialog emotion
datasets, Friends, EmotionPush, and EmoryNLP, demonstrate that our proposed
hierarchical transformer network models achieve 1.98%, 2.83%, and 3.94%
improvement, respec-tively, over the state-of-the-art methods on each dataset
in terms of macro-F1.
| 2,020 | Computation and Language |
Text Classification with Lexicon from PreAttention Mechanism | A comprehensive and high-quality lexicon plays a crucial role in traditional
text classification approaches. And it improves the utilization of the
linguistic knowledge. Although it is helpful for the task, the lexicon has got
little attention in recent neural network models. Firstly, getting a
high-quality lexicon is not easy. We lack an effective automated lexicon
extraction method, and most lexicons are hand crafted, which is very
inefficient for big data. What's more, there is no an effective way to use a
lexicon in a neural network. To address those limitations, we propose a
Pre-Attention mechanism for text classification in this paper, which can learn
attention of different words according to their effects in the classification
tasks. The words with different attention can form a domain lexicon.
Experiments on three benchmark text classification tasks show that our models
get competitive result comparing with the state-of-the-art methods. We get
90.5% accuracy on Stanford Large Movie Review dataset, 82.3% on Subjectivity
dataset, 93.7% on Movie Reviews. And compared with the text classification
model without Pre-Attention mechanism, those with Pre-Attention mechanism
improve by 0.9%-2.4% accuracy, which proves the validity of the Pre-Attention
mechanism. In addition, the Pre-Attention mechanism performs well followed by
different types of neural networks (e.g., convolutional neural networks and
Long Short-Term Memory networks). For the same dataset, when we use
Pre-Attention mechanism to get attention value followed by different neural
networks, those words with high attention values have a high degree of
coincidence, which proves the versatility and portability of the Pre-Attention
mechanism. we can get stable lexicons by attention values, which is an
inspiring method of information extraction.
| 2,020 | Computation and Language |
Neural Relation Prediction for Simple Question Answering over Knowledge
Graph | Knowledge graphs are widely used as a typical resource to provide answers to
factoid questions. In simple question answering over knowledge graphs, relation
extraction aims to predict the relation of a factoid question from a set of
predefined relation types. Most recent methods take advantage of neural
networks to match a question with all predefined relations. In this paper, we
propose an instance-based method to capture the underlying relation of question
and to this aim, we detect matching paraphrases of a new question which share
the same relation, and their corresponding relation is selected as our
prediction. The idea of our model roots in the fact that a relation can be
expressed with various forms of questions while these forms share lexically or
semantically similar terms and concepts. Our experiments on the SimpleQuestions
dataset show that the proposed model achieves better accuracy compared to the
state-of-the-art relation extraction models.
| 2,020 | Computation and Language |
Gradient-Based Adversarial Training on Transformer Networks for
Detecting Check-Worthy Factual Claims | We present a study on the efficacy of adversarial training on transformer
neural network models, with respect to the task of detecting check-worthy
claims. In this work, we introduce the first adversarially-regularized,
transformer-based claim spotter model that achieves state-of-the-art results on
multiple challenging benchmarks. We obtain a 4.70 point F1-score improvement
over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019
Dataset, respectively. In the process, we propose a method to apply adversarial
training to transformer models, which has the potential to be generalized to
many similar text classification tasks. Along with our results, we are
releasing our codebase and manually labeled datasets. We also showcase our
models' real world usage via a live public API.
| 2,020 | Computation and Language |
Learning by Semantic Similarity Makes Abstractive Summarization Better | By harnessing pre-trained language models, summarization models had rapid
progress recently. However, the models are mainly assessed by automatic
evaluation metrics such as ROUGE. Although ROUGE is known for having a positive
correlation with human evaluation scores, it has been criticized for its
vulnerability and the gap between actual qualities. In this paper, we compare
the generated summaries from recent LM, BART, and the reference summaries from
a benchmark dataset, CNN/DM, using a crowd-sourced human evaluation metric.
Interestingly, model-generated summaries receive higher scores relative to
reference summaries. Stemming from our experimental results, we first argue the
intrinsic characteristics of the CNN/DM dataset, the progress of pre-trained
language models, and their ability to generalize on the training data. Finally,
we share our insights into the model-generated summaries and presents our
thought on learning methods for abstractive summarization.
| 2,021 | Computation and Language |
An enhanced Tree-LSTM architecture for sentence semantic modeling using
typed dependencies | Tree-based Long short term memory (LSTM) network has become state-of-the-art
for modeling the meaning of language texts as they can effectively exploit the
grammatical syntax and thereby non-linear dependencies among words of the
sentence. However, most of these models cannot recognize the difference in
meaning caused by a change in semantic roles of words or phrases because they
do not acknowledge the type of grammatical relations, also known as typed
dependencies, in sentence structure. This paper proposes an enhanced LSTM
architecture, called relation gated LSTM, which can model the relationship
between two inputs of a sequence using a control input. We also introduce a
Tree-LSTM model called Typed Dependency Tree-LSTM that uses the sentence
dependency parse structure as well as the dependency type to embed sentence
meaning into a dense vector. The proposed model outperformed its type-unaware
counterpart in two typical NLP tasks - Semantic Relatedness Scoring and
Sentiment Analysis, in a lesser number of training epochs. The results were
comparable or competitive with other state-of-the-art models. Qualitative
analysis showed that changes in the voice of sentences had little effect on the
model's predicted scores, while changes in nominal (noun) words had a more
significant impact. The model recognized subtle semantic relationships in
sentence pairs. The magnitudes of learned typed dependencies embeddings were
also in agreement with human intuitions. The research findings imply the
significance of grammatical relations in sentence modeling. The proposed models
would serve as a base for future researches in this direction.
| 2,020 | Computation and Language |
Interpretable Multi-Headed Attention for Abstractive Summarization at
Controllable Lengths | Abstractive summarization at controllable lengths is a challenging task in
natural language processing. It is even more challenging for domains where
limited training data is available or scenarios in which the length of the
summary is not known beforehand. At the same time, when it comes to trusting
machine-generated summaries, explaining how a summary was constructed in
human-understandable terms may be critical. We propose Multi-level Summarizer
(MLS), a supervised method to construct abstractive summaries of a text
document at controllable lengths. The key enabler of our method is an
interpretable multi-headed attention mechanism that computes attention
distribution over an input document using an array of timestep independent
semantic kernels. Each kernel optimizes a human-interpretable syntactic or
semantic property. Exhaustive experiments on two low-resource datasets in the
English language show that MLS outperforms strong baselines by up to 14.70% in
the METEOR score. Human evaluation of the summaries also suggests that they
capture the key concepts of the document at various length-budgets.
| 2,020 | Computation and Language |
Studying the Effects of Cognitive Biases in Evaluation of Conversational
Agents | Humans quite frequently interact with conversational agents. The rapid
advancement in generative language modeling through neural networks has helped
advance the creation of intelligent conversational agents. Researchers
typically evaluate the output of their models through crowdsourced judgments,
but there are no established best practices for conducting such studies.
Moreover, it is unclear if cognitive biases in decision-making are affecting
crowdsourced workers' judgments when they undertake these tasks. To
investigate, we conducted a between-subjects study with 77 crowdsourced workers
to understand the role of cognitive biases, specifically anchoring bias, when
humans are asked to evaluate the output of conversational agents. Our results
provide insight into how best to evaluate conversational agents. We find
increased consistency in ratings across two experimental conditions may be a
result of anchoring bias. We also determine that external factors such as time
and prior experience in similar tasks have effects on inter-rater consistency.
| 2,020 | Computation and Language |
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural
Language Understanding | We present MT-DNN, an open-source natural language understanding (NLU)
toolkit that makes it easy for researchers and developers to train customized
deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed
to facilitate rapid customization for a broad spectrum of NLU tasks, using a
variety of objectives (classification, regression, structured prediction) and
text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is
its built-in support for robust and transferable learning using the adversarial
multi-task learning paradigm. To enable efficient production deployment, MT-DNN
supports multi-task knowledge distillation, which can substantially compress a
deep neural model without significant performance drop. We demonstrate the
effectiveness of MT-DNN on a wide range of NLU applications across general and
biomedical domains. The software and pre-trained models will be publicly
available at https://github.com/namisan/mt-dnn.
| 2,020 | Computation and Language |
Towards Making the Most of Context in Neural Machine Translation | Document-level machine translation manages to outperform sentence level
models by a small margin, but have failed to be widely adopted. We argue that
previous research did not make a clear use of the global context, and propose a
new document-level NMT framework that deliberately models the local context of
each sentence with the awareness of the global context of the document in both
source and target languages. We specifically design the model to be able to
deal with documents containing any number of sentences, including single
sentences. This unified approach allows our model to be trained elegantly on
standard datasets without needing to train on sentence and document level data
separately. Experimental results demonstrate that our model outperforms
Transformer baselines and previous document-level NMT models with substantial
margins of up to 2.1 BLEU on state-of-the-art baselines. We also provide
analyses which show the benefit of context far beyond the neighboring two or
three sentences, which previous studies have typically incorporated.
| 2,020 | Computation and Language |
Non-Autoregressive Dialog State Tracking | Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues
have progressed toward open-vocabulary or generation-based approaches where the
models can generate slot value candidates from the dialogue history itself.
These approaches have shown good performance gain, especially in complicated
dialogue domains with dynamic slot values. However, they fall short in two
aspects: (1) they do not allow models to explicitly learn signals across
domains and slots to detect potential dependencies among (domain, slot) pairs;
and (2) existing models follow auto-regressive approaches which incur high time
cost when the dialogue evolves over multiple domains and multiple turns. In
this paper, we propose a novel framework of Non-Autoregressive Dialog State
Tracking (NADST) which can factor in potential dependencies among domains and
slots to optimize the models towards better prediction of dialogue states as a
complete set rather than separate slots. In particular, the non-autoregressive
nature of our method not only enables decoding in parallel to significantly
reduce the latency of DST for real-time dialogue response generation, but also
detect dependencies among slots at token level in addition to slot and domain
level. Our empirical results show that our model achieves the state-of-the-art
joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency
of our model is an order of magnitude lower than the previous state of the art
as the dialogue history extends over time.
| 2,020 | Computation and Language |
LAMBERT: Layout-Aware (Language) Modeling for information extraction | We introduce a simple new approach to the problem of understanding documents
where non-trivial layout influences the local semantics. To this end, we modify
the Transformer encoder architecture in a way that allows it to use layout
features obtained from an OCR system, without the need to re-learn language
semantics from scratch. We only augment the input of the model with the
coordinates of token bounding boxes, avoiding, in this way, the use of raw
images. This leads to a layout-aware language model which can then be
fine-tuned on downstream tasks.
The model is evaluated on an end-to-end information extraction task using
four publicly available datasets: Kleister NDA, Kleister Charity, SROIE and
CORD. We show that our model achieves superior performance on datasets
consisting of visually rich documents, while also outperforming the baseline
RoBERTa on documents with flat layout (NDA \(F_{1}\) increase from 78.50 to
80.42). Our solution ranked first on the public leaderboard for the Key
Information Extraction from the SROIE dataset, improving the SOTA
\(F_{1}\)-score from 97.81 to 98.17.
| 2,021 | Computation and Language |
Rnn-transducer with language bias for end-to-end Mandarin-English
code-switching speech recognition | Recently, language identity information has been utilized to improve the
performance of end-to-end code-switching (CS) speech recognition. However,
previous works use an additional language identification (LID) model as an
auxiliary module, which causes the system complex. In this work, we propose an
improved recurrent neural network transducer (RNN-T) model with language bias
to alleviate the problem. We use the language identities to bias the model to
predict the CS points. This promotes the model to learn the language identity
information directly from transcription, and no additional LID model is needed.
We evaluate the approach on a Mandarin-English CS corpus SEAME. Compared to our
RNN-T baseline, the proposed method can achieve 16.2% and 12.9% relative error
reduction on two test sets, respectively.
| 2,020 | Computation and Language |
A Systematic Comparison of Architectures for Document-Level Sentiment
Classification | Documents are composed of smaller pieces - paragraphs, sentences, and tokens
- that have complex relationships between one another. Sentiment classification
models that take into account the structure inherent in these documents have a
theoretical advantage over those that do not. At the same time, transfer
learning models based on language model pretraining have shown promise for
document classification. However, these two paradigms have not been
systematically compared and it is not clear under which circumstances one
approach is better than the other. In this work we empirically compare
hierarchical models and transfer learning for document-level sentiment
classification. We show that non-trivial hierarchical models outperform
previous baselines and transfer learning on document-level sentiment
classification in five languages.
| 2,022 | Computation and Language |
CodeBERT: A Pre-Trained Model for Programming and Natural Languages | We present CodeBERT, a bimodal pre-trained model for programming language
(PL) and nat-ural language (NL). CodeBERT learns general-purpose
representations that support downstream NL-PL applications such as natural
language codesearch, code documentation generation, etc. We develop CodeBERT
with Transformer-based neural architecture, and train it with a hybrid
objective function that incorporates the pre-training task of replaced token
detection, which is to detect plausible alternatives sampled from generators.
This enables us to utilize both bimodal data of NL-PL pairs and unimodal data,
where the former provides input tokens for model training while the latter
helps to learn better generators. We evaluate CodeBERT on two NL-PL
applications by fine-tuning model parameters. Results show that CodeBERT
achieves state-of-the-art performance on both natural language code search and
code documentation generation tasks. Furthermore, to investigate what type of
knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and
evaluate in a zero-shot setting where parameters of pre-trained models are
fixed. Results show that CodeBERT performs better than previous pre-trained
models on NL-PL probing.
| 2,020 | Computation and Language |
Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection
and Sentiment Analysis in Conversation | Sentiment Analysis and Emotion Detection in conversation is key in several
real-world applications, with an increase in modalities available aiding a
better understanding of the underlying emotions. Multi-modal Emotion Detection
and Sentiment Analysis can be particularly useful, as applications will be able
to use specific subsets of available modalities, as per the available data.
Current systems dealing with Multi-modal functionality fail to leverage and
capture - the context of the conversation through all modalities, the
dependency between the listener(s) and speaker emotional states, and the
relevance and relationship between the available modalities. In this paper, we
propose an end to end RNN architecture that attempts to take into account all
the mentioned drawbacks. Our proposed model, at the time of writing,
out-performs the state of the art on a benchmark dataset on a variety of
accuracy and regression metrics.
| 2,020 | Computation and Language |
Compressing BERT: Studying the Effects of Weight Pruning on Transfer
Learning | Pre-trained universal feature extractors, such as BERT for natural language
processing and VGG for computer vision, have become effective methods for
improving deep learning models without requiring more labeled data. While
effective, feature extractors like BERT may be prohibitively large for some
deployment scenarios. We explore weight pruning for BERT and ask: how does
compression during pre-training affect transfer learning? We find that pruning
affects transfer learning in three broad regimes. Low levels of pruning
(30-40%) do not affect pre-training loss or transfer to downstream tasks at
all. Medium levels of pruning increase the pre-training loss and prevent useful
pre-training information from being transferred to downstream tasks. High
levels of pruning additionally prevent models from fitting downstream datasets,
leading to further degradation. Finally, we observe that fine-tuning BERT on a
specific task does not improve its prunability. We conclude that BERT can be
pruned once during pre-training rather than separately for each task without
affecting performance.
| 2,020 | Computation and Language |
Federated pretraining and fine tuning of BERT using clinical notes from
multiple silos | Large scale contextual representation models, such as BERT, have
significantly advanced natural language processing (NLP) in recently years.
However, in certain area like healthcare, accessing diverse large scale text
data from multiple institutions is extremely challenging due to privacy and
regulatory reasons. In this article, we show that it is possible to both
pretrain and fine tune BERT models in a federated manner using clinical texts
from different silos without moving the data.
| 2,020 | Computation and Language |
FrameAxis: Characterizing Microframe Bias and Intensity with Word
Embedding | Framing is a process of emphasizing a certain aspect of an issue over the
others, nudging readers or listeners towards different positions on the issue
even without making a biased argument. {Here, we propose FrameAxis, a method
for characterizing documents by identifying the most relevant semantic axes
("microframes") that are overrepresented in the text using word embedding. Our
unsupervised approach can be readily applied to large datasets because it does
not require manual annotations. It can also provide nuanced insights by
considering a rich set of semantic axes. FrameAxis is designed to
quantitatively tease out two important dimensions of how microframes are used
in the text. \textit{Microframe bias} captures how biased the text is on a
certain microframe, and \textit{microframe intensity} shows how actively a
certain microframe is used. Together, they offer a detailed characterization of
the text. We demonstrate that microframes with the highest bias and intensity
well align with sentiment, topic, and partisan spectrum by applying FrameAxis
to multiple datasets from restaurant reviews to political news.} The existing
domain knowledge can be incorporated into FrameAxis {by using custom
microframes and by using FrameAxis as an iterative exploratory analysis
instrument.} Additionally, we propose methods for explaining the results of
FrameAxis at the level of individual words and documents. Our method may
accelerate scalable and sophisticated computational analyses of framing across
disciplines.
| 2,021 | Computation and Language |
Balancing Cost and Benefit with Tied-Multi Transformers | We propose and evaluate a novel procedure for training multiple Transformers
with tied parameters which compresses multiple models into one enabling the
dynamic choice of the number of encoder and decoder layers during decoding. In
sequence-to-sequence modeling, typically, the output of the last layer of the
N-layer encoder is fed to the M-layer decoder, and the output of the last
decoder layer is used to compute loss. Instead, our method computes a single
loss consisting of NxM losses, where each loss is computed from the output of
one of the M decoder layers connected to one of the N encoder layers. Such a
model subsumes NxM models with different number of encoder and decoder layers,
and can be used for decoding with fewer than the maximum number of encoder and
decoder layers. We then propose a mechanism to choose a priori the number of
encoder and decoder layers for faster decoding, and also explore recurrent
stacking of layers and knowledge distillation for model compression. We present
a cost-benefit analysis of applying the proposed approaches for neural machine
translation and show that they reduce decoding costs while preserving
translation quality.
| 2,020 | Computation and Language |
Guiding attention in Sequence-to-sequence models for Dialogue Act
prediction | The task of predicting dialog acts (DA) based on conversational dialog is a
key component in the development of conversational agents. Accurately
predicting DAs requires a precise modeling of both the conversation and the
global tag dependencies. We leverage seq2seq approaches widely adopted in
Neural Machine Translation (NMT) to improve the modelling of tag sequentiality.
Seq2seq models are known to learn complex global dependencies while currently
proposed approaches using linear conditional random fields (CRF) only model
local tag dependencies. In this work, we introduce a seq2seq model tailored for
DA classification using: a hierarchical encoder, a novel guided attention
mechanism and beam search applied to both training and inference. Compared to
the state of the art our model does not require handcrafted features and is
trained end-to-end. Furthermore, the proposed approach achieves an unmatched
accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on
MRDA.
| 2,020 | Computation and Language |
Contextual Lensing of Universal Sentence Representations | What makes a universal sentence encoder universal? The notion of a generic
encoder of text appears to be at odds with the inherent contextualization and
non-permanence of language use in a dynamic world. However, mapping sentences
into generic fixed-length vectors for downstream similarity and retrieval tasks
has been fruitful, particularly for multilingual applications. How do we manage
this dilemma? In this work we propose Contextual Lensing, a methodology for
inducing context-oriented universal sentence vectors. We break the construction
of universal sentence vectors into a core, variable length, sentence matrix
representation equipped with an adaptable `lens' from which fixed-length
vectors can be induced as a function of the lens context. We show that it is
possible to focus notions of language similarity into a small number of lens
parameters given a core universal matrix representation. For example, we
demonstrate the ability to encode translation similarity of sentences across
several languages into a single weight matrix, even when the core encoder has
not seen parallel data.
| 2,020 | Computation and Language |
The Fluidity of Concept Representations in Human Brain Signals | Cognitive theories of human language processing often distinguish between
concrete and abstract concepts. In this work, we analyze the discriminability
of concrete and abstract concepts in fMRI data using a range of analysis
methods. We find that the distinction can be decoded from the signal with an
accuracy significantly above chance, but it is not found to be a relevant
structuring factor in clustering and relational analyses. From our detailed
comparison, we obtain the impression that human concept representations are
more fluid than dichotomous categories can capture. We argue that fluid concept
representations lead to more realistic models of human language processing
because they better capture the ambiguity and underspecification present in
natural language use.
| 2,020 | Computation and Language |
MA-DST: Multi-Attention Based Scalable Dialog State Tracking | Task oriented dialog agents provide a natural language interface for users to
complete their goal. Dialog State Tracking (DST), which is often a core
component of these systems, tracks the system's understanding of the user's
goal throughout the conversation. To enable accurate multi-domain DST, the
model needs to encode dependencies between past utterances and slot semantics
and understand the dialog context, including long-range cross-domain
references. We introduce a novel architecture for this task to encode the
conversation history and slot semantics more robustly by using attention
mechanisms at multiple granularities. In particular, we use cross-attention to
model relationships between the context and slots at different semantic levels
and self-attention to resolve cross-domain coreferences. In addition, our
proposed architecture does not rely on knowing the domain ontologies beforehand
and can also be used in a zero-shot setting for new domains or unseen slot
values. Our model improves the joint goal accuracy by 5% (absolute) in the
full-data setting and by up to 2% (absolute) in the zero-shot setting over the
present state-of-the-art on the MultiWoZ 2.1 dataset.
| 2,020 | Computation and Language |
Compositional Neural Machine Translation by Removing the Lexicon from
Syntax | The meaning of a natural language utterance is largely determined from its
syntax and words. Additionally, there is evidence that humans process an
utterance by separating knowledge about the lexicon from syntax knowledge.
Theories from semantics and neuroscience claim that complete word meanings are
not encoded in the representation of syntax. In this paper, we propose neural
units that can enforce this constraint over an LSTM encoder and decoder. We
demonstrate that our model achieves competitive performance across a variety of
domains including semantic parsing, syntactic parsing, and English to Mandarin
Chinese translation. In these cases, our model outperforms the standard LSTM
encoder and decoder architecture on many or all of our metrics. To demonstrate
that our model achieves the desired separation between the lexicon and syntax,
we analyze its weights and explore its behavior when different neural modules
are damaged. When damaged, we find that the model displays the knowledge
distortions that aphasics are evidenced to have.
| 2,020 | Computation and Language |
Identifying physical health comorbidities in a cohort of individuals
with severe mental illness: An application of SemEHR | Multimorbidity research in mental health services requires data from physical
health conditions which is traditionally limited in mental health care
electronic health records. In this study, we aimed to extract data from
physical health conditions from clinical notes using SemEHR. Data was extracted
from Clinical Record Interactive Search (CRIS) system at South London and
Maudsley Biomedical Research Centre (SLaM BRC) and the cohort consisted of all
individuals who had received a primary or secondary diagnosis of severe mental
illness between 2007 and 2018. Three pairs of annotators annotated 2403
documents with an average Cohen's Kappa of 0.757. Results show that the NLP
performance varies across different diseases areas (F1 0.601 - 0.954)
suggesting that the language patterns or terminologies of different condition
groups entail different technical challenges to the same NLP task.
| 2,020 | Computation and Language |
Application of Pre-training Models in Named Entity Recognition | Named Entity Recognition (NER) is a fundamental Natural Language Processing
(NLP) task to extract entities from unstructured data. The previous methods for
NER were based on machine learning or deep learning. Recently, pre-training
models have significantly improved performance on multiple NLP tasks. In this
paper, firstly, we introduce the architecture and pre-training tasks of four
common pre-training models: BERT, ERNIE, ERNIE2.0-tiny, and RoBERTa. Then, we
apply these pre-training models to a NER task by fine-tuning, and compare the
effects of the different model architecture and pre-training tasks on the NER
task. The experiment results showed that RoBERTa achieved state-of-the-art
results on the MSRA-2006 dataset.
| 2,020 | Computation and Language |
REALM: Retrieval-Augmented Language Model Pre-Training | Language model pre-training has been shown to capture a surprising amount of
world knowledge, crucial for NLP tasks such as question answering. However,
this knowledge is stored implicitly in the parameters of a neural network,
requiring ever-larger networks to cover more facts.
To capture knowledge in a more modular and interpretable way, we augment
language model pre-training with a latent knowledge retriever, which allows the
model to retrieve and attend over documents from a large corpus such as
Wikipedia, used during pre-training, fine-tuning and inference. For the first
time, we show how to pre-train such a knowledge retriever in an unsupervised
manner, using masked language modeling as the learning signal and
backpropagating through a retrieval step that considers millions of documents.
We demonstrate the effectiveness of Retrieval-Augmented Language Model
pre-training (REALM) by fine-tuning on the challenging task of Open-domain
Question Answering (Open-QA). We compare against state-of-the-art models for
both explicit and implicit knowledge storage on three popular Open-QA
benchmarks, and find that we outperform all previous methods by a significant
margin (4-16% absolute accuracy), while also providing qualitative benefits
such as interpretability and modularity.
| 2,020 | Computation and Language |
How Much Knowledge Can You Pack Into the Parameters of a Language Model? | It has recently been observed that neural language models trained on
unstructured text can implicitly store and retrieve knowledge using natural
language queries. In this short paper, we measure the practical utility of this
approach by fine-tuning pre-trained models to answer questions without access
to any external context or knowledge. We show that this approach scales with
model size and performs competitively with open-domain systems that explicitly
retrieve answers from an external knowledge source when answering questions. To
facilitate reproducibility and future work, we release our code and trained
models at https://goo.gle/t5-cbqa.
| 2,020 | Computation and Language |
Measuring Social Biases in Grounded Vision and Language Embeddings | We generalize the notion of social biases from language embeddings to
grounded vision and language embeddings. Biases are present in grounded
embeddings, and indeed seem to be equally or more significant than for
ungrounded embeddings. This is despite the fact that vision and language can
suffer from different biases, which one might hope could attenuate the biases
in both. Multiple ways exist to generalize metrics measuring bias in word
embeddings to this new setting. We introduce the space of generalizations
(Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations
answer different yet important questions about how biases, language, and vision
interact. These metrics are used on a new dataset, the first for grounded bias,
created by augmenting extending standard linguistic bias benchmarks with 10,228
images from COCO, Conceptual Captions, and Google Images. Dataset construction
is challenging because vision datasets are themselves very biased. The presence
of these biases in systems will begin to have real-world consequences as they
are deployed, making carefully measuring bias and then mitigating it critical
to building a fair society.
| 2,023 | Computation and Language |
On the impressive performance of randomly weighted encoders in
summarization tasks | In this work, we investigate the performance of untrained randomly
initialized encoders in a general class of sequence to sequence models and
compare their performance with that of fully-trained encoders on the task of
abstractive summarization. We hypothesize that random projections of an input
text have enough representational power to encode the hierarchical structure of
sentences and semantics of documents. Using a trained decoder to produce
abstractive text summaries, we empirically demonstrate that architectures with
untrained randomly initialized encoders perform competitively with respect to
the equivalent architectures with fully-trained encoders. We further find that
the capacity of the encoder not only improves overall model generalization but
also closes the performance gap between untrained randomly initialized and
full-trained encoders. To our knowledge, it is the first time that general
sequence to sequence models with attention are assessed for trained and
randomly projected representations on abstractive summarization.
| 2,020 | Computation and Language |
Learning Dynamic Belief Graphs to Generalize on Text-Based Games | Playing text-based games requires skills in processing natural language and
sequential decision making. Achieving human-level performance on text-based
games remains an open challenge, and prior research has largely relied on
hand-crafted structured representations and heuristics. In this work, we
investigate how an agent can plan and generalize in text-based games using
graph-structured representations learned end-to-end from raw text. We propose a
novel graph-aided transformer agent (GATA) that infers and updates latent
belief graphs during planning to enable effective action selection by capturing
the underlying game dynamics. GATA is trained using a combination of
reinforcement and self-supervised learning. Our work demonstrates that the
learned graph-based representations help agents converge to better policies
than their text-only counterparts and facilitate effective generalization
across game configurations. Experiments on 500+ unique games from the TextWorld
suite show that our best agent outperforms text-based baselines by an average
of 24.2%.
| 2,021 | Computation and Language |
Refinement of Unsupervised Cross-Lingual Word Embeddings | Cross-lingual word embeddings aim to bridge the gap between high-resource and
low-resource languages by allowing to learn multilingual word representations
even without using any direct bilingual signal. The lion's share of the methods
are projection-based approaches that map pre-trained embeddings into a shared
latent space. These methods are mostly based on the orthogonal transformation,
which assumes language vector spaces to be isomorphic. However, this criterion
does not necessarily hold, especially for morphologically-rich languages. In
this paper, we propose a self-supervised method to refine the alignment of
unsupervised bilingual word embeddings. The proposed model moves vectors of
words and their corresponding translations closer to each other as well as
enforces length- and center-invariance, thus allowing to better align
cross-lingual embeddings. The experimental results demonstrate the
effectiveness of our approach, as in most cases it outperforms state-of-the-art
methods in a bilingual lexicon induction task.
| 2,020 | Computation and Language |
Is Aligning Embedding Spaces a Challenging Task? A Study on
Heterogeneous Embedding Alignment Methods | Representation Learning of words and Knowledge Graphs (KG) into low
dimensional vector spaces along with its applications to many real-world
scenarios have recently gained momentum. In order to make use of multiple KG
embeddings for knowledge-driven applications such as question answering, named
entity disambiguation, knowledge graph completion, etc., alignment of different
KG embedding spaces is necessary. In addition to multilinguality and
domain-specific information, different KGs pose the problem of structural
differences making the alignment of the KG embeddings more challenging. This
paper provides a theoretical analysis and comparison of the state-of-the-art
alignment methods between two embedding spaces representing entity-entity and
entity-word. This paper also aims at assessing the capability and short-comings
of the existing alignment methods on the pretext of different applications.
| 2,020 | Computation and Language |
Guider l'attention dans les modeles de sequence a sequence pour la
prediction des actes de dialogue | The task of predicting dialog acts (DA) based on conversational dialog is a
key component in the development of conversational agents. Accurately
predicting DAs requires a precise modeling of both the conversation and the
global tag dependencies. We leverage seq2seq approaches widely adopted in
Neural Machine Translation (NMT) to improve the modelling of tag sequentiality.
Seq2seq models are known to learn complex global dependencies while currently
proposed approaches using linear conditional random fields (CRF) only model
local tag dependencies. In this work, we introduce a seq2seq model tailored for
DA classification using: a hierarchical encoder, a novel guided attention
mechanism and beam search applied to both training and inference. Compared to
the state of the art our model does not require handcrafted features and is
trained end-to-end. Furthermore, the proposed approach achieves an unmatched
accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on
MRDA.
| 2,020 | Computation and Language |
Modelling Latent Skills for Multitask Language Generation | We present a generative model for multitask conditional language generation.
Our guiding hypothesis is that a shared set of latent skills underlies many
disparate language generation tasks, and that explicitly modelling these skills
in a task embedding space can help with both positive transfer across tasks and
with efficient adaptation to new tasks. We instantiate this task embedding
space as a latent variable in a latent variable sequence-to-sequence model. We
evaluate this hypothesis by curating a series of monolingual text-to-text
language generation datasets - covering a broad range of tasks and domains -
and comparing the performance of models both in the multitask and few-shot
regimes. We show that our latent task variable model outperforms other
sequence-to-sequence baselines on average across tasks in the multitask
setting. In the few-shot learning setting on an unseen test dataset (i.e., a
new task), we demonstrate that model adaptation based on inference in the
latent task space is more robust than standard fine-tuning based parameter
adaptation and performs comparably in terms of overall performance. Finally, we
examine the latent task representations learnt by our model and show that they
cluster tasks in a natural way.
| 2,020 | Computation and Language |
Extracting and Validating Explanatory Word Archipelagoes using Dual
Entropy | The logical connectivity of text is represented by the connectivity of words
that form archipelagoes. Here, each archipelago is a sequence of islands of the
occurrences of a certain word. An island here means the local sequence of
sentences where the word is emphasized, and an archipelago of a length
comparable to the target text is extracted using the co-variation of entropy A
(the window-based entropy) on the distribution of the word's occurrences with
the width of each time window. Then, the logical connectivity of text is
evaluated on entropy B (the graph-based entropy) computed on the distribution
of sentences to connected word-clusters obtained on the co-occurrence of words.
The results show the parts of the target text with words forming archipelagoes
extracted on entropy A, without learned or prepared knowledge, form an
explanatory part of the text that is of smaller entropy B than the parts
extracted by the baseline methods.
| 2,020 | Computation and Language |
Training Question Answering Models From Synthetic Data | Question and answer generation is a data augmentation method that aims to
improve question answering (QA) models given the limited amount of human
labeled data. However, a considerable gap remains between synthetic and
human-generated question-answer pairs. This work aims to narrow this gap by
taking advantage of large language models and explores several factors such as
model size, quality of pretrained models, scale of data synthesized, and
algorithmic choices. On the SQuAD1.1 question answering task, we achieve higher
accuracy using solely synthetic questions and answers than when using the
SQuAD1.1 training set questions alone. Removing access to real Wikipedia data,
we synthesize questions and answers from a synthetic corpus generated by an 8.3
billion parameter GPT-2 model. With no access to human supervision and only
access to other models, we are able to train state of the art question
answering networks on entirely model-generated data that achieve 88.4 Exact
Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. We further apply our
methodology to SQuAD2.0 and show a 2.8 absolute gain on EM score compared to
prior work using synthetic data.
| 2,020 | Computation and Language |
Emergent Communication with World Models | We introduce Language World Models, a class of language-conditional
generative model which interpret natural language messages by predicting latent
codes of future observations. This provides a visual grounding of the message,
similar to an enhanced observation of the world, which may include objects
outside of the listening agent's field-of-view. We incorporate this
"observation" into a persistent memory state, and allow the listening agent's
policy to condition on it, akin to the relationship between memory and
controller in a World Model. We show this improves effective communication and
task success in 2D gridworld speaker-listener navigation tasks. In addition, we
develop two losses framed specifically for our model-based formulation to
promote positive signalling and positive listening. Finally, because messages
are interpreted in a generative model, we can visualize the model beliefs to
gain insight into how the communication channel is utilized.
| 2,020 | Computation and Language |
"Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior
Using Imagine-Then-Arbitrate Model | Producing natural and accurate responses like human beings is the ultimate
goal of intelligent dialogue agents. So far, most of the past works concentrate
on selecting or generating one pertinent and fluent response according to
current query and its context. These models work on a one-to-one environment,
making one response to one utterance each round. However, in real human-human
conversations, human often sequentially sends several short messages for
readability instead of a long message in one turn. Thus messages will not end
with an explicit ending signal, which is crucial for agents to decide when to
reply. So the first step for an intelligent dialogue agent is not replying but
deciding if it should reply at the moment. To address this issue, in this
paper, we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to
help the agent decide whether to wait or to make a response directly. Our
method has two imaginator modules and an arbitrator module. The two imaginators
will learn the agent's and user's speaking style respectively, generate
possible utterances as the input of the arbitrator, combining with dialogue
history. And the arbitrator decides whether to wait or to make a response to
the user directly. To verify the performance and effectiveness of our method,
we prepared two dialogue datasets and compared our approach with several
popular models. Experimental results show that our model performs well on
addressing ending prediction issue and outperforms baseline models.
| 2,021 | Computation and Language |
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