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A Dataset of German Legal Documents for Named Entity Recognition | We describe a dataset developed for Named Entity Recognition in German
federal court decisions. It consists of approx. 67,000 sentences with over 2
million tokens. The resource contains 54,000 manually annotated entities,
mapped to 19 fine-grained semantic classes: person, judge, lawyer, country,
city, street, landscape, organization, company, institution, court, brand, law,
ordinance, European legal norm, regulation, contract, court decision, and legal
literature. The legal documents were, furthermore, automatically annotated with
more than 35,000 TimeML-based time expressions. The dataset, which is available
under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training
an NER service for German legal documents in the EU project Lynx.
| 2,020 | Computation and Language |
Abstractive Text Summarization based on Language Model Conditioning and
Locality Modeling | We explore to what extent knowledge about the pre-trained language model that
is used is beneficial for the task of abstractive summarization. To this end,
we experiment with conditioning the encoder and decoder of a Transformer-based
neural model on the BERT language model. In addition, we propose a new method
of BERT-windowing, which allows chunk-wise processing of texts longer than the
BERT window size. We also explore how locality modelling, i.e., the explicit
restriction of calculations to the local context, can affect the summarization
ability of the Transformer. This is done by introducing 2-dimensional
convolutional self-attention into the first layers of the encoder. The results
of our models are compared to a baseline and the state-of-the-art models on the
CNN/Daily Mail dataset. We additionally train our model on the SwissText
dataset to demonstrate usability on German. Both models outperform the baseline
in ROUGE scores on two datasets and show its superiority in a manual
qualitative analysis.
| 2,020 | Computation and Language |
Abstractive Summarization with Combination of Pre-trained
Sequence-to-Sequence and Saliency Models | Pre-trained sequence-to-sequence (seq-to-seq) models have significantly
improved the accuracy of several language generation tasks, including
abstractive summarization. Although the fluency of abstractive summarization
has been greatly improved by fine-tuning these models, it is not clear whether
they can also identify the important parts of the source text to be included in
the summary. In this study, we investigated the effectiveness of combining
saliency models that identify the important parts of the source text with the
pre-trained seq-to-seq models through extensive experiments. We also proposed a
new combination model consisting of a saliency model that extracts a token
sequence from a source text and a seq-to-seq model that takes the sequence as
an additional input text. Experimental results showed that most of the
combination models outperformed a simple fine-tuned seq-to-seq model on both
the CNN/DM and XSum datasets even if the seq-to-seq model is pre-trained on
large-scale corpora. Moreover, for the CNN/DM dataset, the proposed combination
model exceeded the previous best-performed model by 1.33 points on ROUGE-L.
| 2,020 | Computation and Language |
Named Entities in Medical Case Reports: Corpus and Experiments | We present a new corpus comprising annotations of medical entities in case
reports, originating from PubMed Central's open access library. In the case
reports, we annotate cases, conditions, findings, factors and negation
modifiers. Moreover, where applicable, we annotate relations between these
entities. As such, this is the first corpus of this kind made available to the
scientific community in English. It enables the initial investigation of
automatic information extraction from case reports through tasks like Named
Entity Recognition, Relation Extraction and (sentence/paragraph) relevance
detection. Additionally, we present four strong baseline systems for the
detection of medical entities made available through the annotated dataset.
| 2,020 | Computation and Language |
Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency
Parsing with Iterative Refinement | We propose the Recursive Non-autoregressive Graph-to-Graph Transformer
architecture (RNGTr) for the iterative refinement of arbitrary graphs through
the recursive application of a non-autoregressive Graph-to-Graph Transformer
and apply it to syntactic dependency parsing. We demonstrate the power and
effectiveness of RNGTr on several dependency corpora, using a refinement model
pre-trained with BERT. We also introduce Syntactic Transformer (SynTr), a
non-recursive parser similar to our refinement model. RNGTr can improve the
accuracy of a variety of initial parsers on 13 languages from the Universal
Dependencies Treebanks, English and Chinese Penn Treebanks, and the German
CoNLL2009 corpus, even improving over the new state-of-the-art results achieved
by SynTr, significantly improving the state-of-the-art for all corpora tested.
| 2,021 | Computation and Language |
InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining | Multi-modal pretraining for learning high-level multi-modal representation is
a further step towards deep learning and artificial intelligence. In this work,
we propose a novel model, namely InterBERT (BERT for Interaction), which is the
first model of our series of multimodal pretraining methods M6
(MultiModality-to-MultiModality Multitask Mega-transformer). The model owns
strong capability of modeling interaction between the information flows of
different modalities. The single-stream interaction module is capable of
effectively processing information of multiple modalilties, and the two-stream
module on top preserves the independence of each modality to avoid performance
downgrade in single-modal tasks. We pretrain the model with three pretraining
tasks, including masked segment modeling (MSM), masked region modeling (MRM)
and image-text matching (ITM); and finetune the model on a series of
vision-and-language downstream tasks. Experimental results demonstrate that
InterBERT outperforms a series of strong baselines, including the most recent
multi-modal pretraining methods, and the analysis shows that MSM and MRM are
effective for pretraining and our method can achieve performances comparable to
BERT in single-modal tasks. Besides, we propose a large-scale dataset for
multi-modal pretraining in Chinese, and we develop the Chinese InterBERT which
is the first Chinese multi-modal pretrained model. We pretrain the Chinese
InterBERT on our proposed dataset of 3.1M image-text pairs from the mobile
Taobao, the largest Chinese e-commerce platform. We finetune the model for
text-based image retrieval, and recently we deployed the model online for
topic-based recommendation.
| 2,021 | Computation and Language |
Learning Contextualized Sentence Representations for Document-Level
Neural Machine Translation | Document-level machine translation incorporates inter-sentential dependencies
into the translation of a source sentence. In this paper, we propose a new
framework to model cross-sentence dependencies by training neural machine
translation (NMT) to predict both the target translation and surrounding
sentences of a source sentence. By enforcing the NMT model to predict source
context, we want the model to learn "contextualized" source sentence
representations that capture document-level dependencies on the source side. We
further propose two different methods to learn and integrate such
contextualized sentence embeddings into NMT: a joint training method that
jointly trains an NMT model with the source context prediction model and a
pre-training & fine-tuning method that pretrains the source context prediction
model on a large-scale monolingual document corpus and then fine-tunes it with
the NMT model. Experiments on Chinese-English and English-German translation
show that both methods can substantially improve the translation quality over a
strong document-level Transformer baseline.
| 2,020 | Computation and Language |
Making Metadata Fit for Next Generation Language Technology Platforms:
The Metadata Schema of the European Language Grid | The current scientific and technological landscape is characterised by the
increasing availability of data resources and processing tools and services. In
this setting, metadata have emerged as a key factor facilitating management,
sharing and usage of such digital assets. In this paper we present ELG-SHARE, a
rich metadata schema catering for the description of Language Resources and
Technologies (processing and generation services and tools, models, corpora,
term lists, etc.), as well as related entities (e.g., organizations, projects,
supporting documents, etc.). The schema powers the European Language Grid
platform that aims to be the primary hub and marketplace for industry-relevant
Language Technology in Europe. ELG-SHARE has been based on various metadata
schemas, vocabularies, and ontologies, as well as related recommendations and
guidelines.
| 2,020 | Computation and Language |
How human judgment impairs automated deception detection performance | Background: Deception detection is a prevalent problem for security
practitioners. With a need for more large-scale approaches, automated methods
using machine learning have gained traction. However, detection performance
still implies considerable error rates. Findings from other domains suggest
that hybrid human-machine integrations could offer a viable path in deception
detection tasks. Method: We collected a corpus of truthful and deceptive
answers about participants' autobiographical intentions (n=1640) and tested
whether a combination of supervised machine learning and human judgment could
improve deception detection accuracy. Human judges were presented with the
outcome of the automated credibility judgment of truthful and deceptive
statements. They could either fully overrule it (hybrid-overrule condition) or
adjust it within a given boundary (hybrid-adjust condition). Results: The data
suggest that in neither of the hybrid conditions did the human judgment add a
meaningful contribution. Machine learning in isolation identified truth-tellers
and liars with an overall accuracy of 69%. Human involvement through
hybrid-overrule decisions brought the accuracy back to the chance level. The
hybrid-adjust condition did not deception detection performance. The
decision-making strategies of humans suggest that the truth bias - the tendency
to assume the other is telling the truth - could explain the detrimental
effect. Conclusion: The current study does not support the notion that humans
can meaningfully add to the deception detection performance of a machine
learning system.
| 2,020 | Computation and Language |
Investigating Language Impact in Bilingual Approaches for Computational
Language Documentation | For endangered languages, data collection campaigns have to accommodate the
challenge that many of them are from oral tradition, and producing
transcriptions is costly. Therefore, it is fundamental to translate them into a
widely spoken language to ensure interpretability of the recordings. In this
paper we investigate how the choice of translation language affects the
posterior documentation work and potential automatic approaches which will work
on top of the produced bilingual corpus. For answering this question, we use
the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56
bilingual pairs that we apply to the task of low-resource unsupervised word
segmentation and alignment. Our results highlight that the choice of language
for translation influences the word segmentation performance, and that
different lexicons are learned by using different aligned translations. Lastly,
this paper proposes a hybrid approach for bilingual word segmentation,
combining boundary clues extracted from a non-parametric Bayesian model
(Goldwater et al., 2009a) with the attentional word segmentation neural model
from Godard et al. (2018). Our results suggest that incorporating these clues
into the neural models' input representation increases their translation and
alignment quality, specially for challenging language pairs.
| 2,020 | Computation and Language |
A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions
for Training Neural Conversation Models | Fully data driven Chatbots for non-goal oriented dialogues are known to
suffer from inconsistent behaviour across their turns, stemming from a general
difficulty in controlling parameters like their assumed background personality
and knowledge of facts. One reason for this is the relative lack of labeled
data from which personality consistency and fact usage could be learned
together with dialogue behaviour. To address this, we introduce a new labeled
dialogue dataset in the domain of movie discussions, where every dialogue is
based on pre-specified facts and opinions. We thoroughly validate the collected
dialogue for adherence of the participants to their given fact and opinion
profile, and find that the general quality in this respect is high. This
process also gives us an additional layer of annotation that is potentially
useful for training models. We introduce as a baseline an end-to-end trained
self-attention decoder model trained on this data and show that it is able to
generate opinionated responses that are judged to be natural and knowledgeable
and show attentiveness.
| 2,020 | Computation and Language |
Empirical Analysis of Zipf's Law, Power Law, and Lognormal Distributions
in Medical Discharge Reports | Bayesian modelling and statistical text analysis rely on informed probability
priors to encourage good solutions. This paper empirically analyses whether
text in medical discharge reports follow Zipf's law, a commonly assumed
statistical property of language where word frequency follows a discrete power
law distribution. We examined 20,000 medical discharge reports from the
MIMIC-III dataset. Methods included splitting the discharge reports into
tokens, counting token frequency, fitting power law distributions to the data,
and testing whether alternative distributions--lognormal, exponential,
stretched exponential, and truncated power law--provided superior fits to the
data. Results show that discharge reports are best fit by the truncated power
law and lognormal distributions. Our findings suggest that Bayesian modelling
and statistical text analysis of discharge report text would benefit from using
truncated power law and lognormal probability priors.
| 2,020 | Computation and Language |
QRMine: A python package for triangulation in Grounded Theory | Grounded theory (GT) is a qualitative research method for building theory
grounded in data. GT uses textual and numeric data and follows various stages
of coding or tagging data for sense-making, such as open coding and selective
coding. Machine Learning (ML) techniques, including natural language processing
(NLP), can assist the researchers in the coding process. Triangulation is the
process of combining various types of data. ML can facilitate deriving insights
from numerical data for corroborating findings from the textual interview
transcripts. We present an open-source python package (QRMine) that
encapsulates various ML and NLP libraries to support coding and triangulation
in GT. QRMine enables researchers to use these methods on their data with
minimal effort. Researchers can install QRMine from the python package index
(PyPI) and can contribute to its development. We believe that the concept of
computational triangulation will make GT relevant in the realm of big data.
| 2,020 | Computation and Language |
European Language Grid: An Overview | With 24 official EU and many additional languages, multilingualism in Europe
and an inclusive Digital Single Market can only be enabled through Language
Technologies (LTs). European LT business is dominated by hundreds of SMEs and a
few large players. Many are world-class, with technologies that outperform the
global players. However, European LT business is also fragmented, by nation
states, languages, verticals and sectors, significantly holding back its
impact. The European Language Grid (ELG) project addresses this fragmentation
by establishing the ELG as the primary platform for LT in Europe. The ELG is a
scalable cloud platform, providing, in an easy-to-integrate way, access to
hundreds of commercial and non-commercial LTs for all European languages,
including running tools and services as well as data sets and resources. Once
fully operational, it will enable the commercial and non-commercial European LT
community to deposit and upload their technologies and data sets into the ELG,
to deploy them through the grid, and to connect with other resources. The ELG
will boost the Multilingual Digital Single Market towards a thriving European
LT community, creating new jobs and opportunities. Furthermore, the ELG project
organises two open calls for up to 20 pilot projects. It also sets up 32
National Competence Centres (NCCs) and the European LT Council (LTC) for
outreach and coordination purposes.
| 2,020 | Computation and Language |
AriEL: volume coding for sentence generation | Mapping sequences of discrete data to a point in a continuous space makes it
difficult to retrieve those sequences via random sampling. Mapping the input to
a volume would make it easier to retrieve at test time, and that's the strategy
followed by the family of approaches based on Variational Autoencoder. However
the fact that they are at the same time optimizing for prediction and for
smoothness of representation, forces them to trade-off between the two. We
improve on the performance of some of the standard methods in deep learning to
generate sentences by uniformly sampling a continuous space. We do it by
proposing AriEL, that constructs volumes in a continuous space, without the
need of encouraging the creation of volumes through the loss function. We first
benchmark on a toy grammar, that allows to automatically evaluate the language
learned and generated by the models. Then, we benchmark on a real dataset of
human dialogues. Our results indicate that the random access to the stored
information is dramatically improved, and our method AriEL is able to generate
a wider variety of correct language by randomly sampling the latent space. VAE
follows in performance for the toy dataset while, AE and Transformer follow for
the real dataset. This partially supports to the hypothesis that encoding
information into volumes instead of into points, can lead to improved retrieval
of learned information with random sampling. This can lead to better generators
and we also discuss potential disadvantages.
| 2,020 | Computation and Language |
Amharic Abstractive Text Summarization | Text Summarization is the task of condensing long text into just a handful of
sentences. Many approaches have been proposed for this task, some of the very
first were building statistical models (Extractive Methods) capable of
selecting important words and copying them to the output, however these models
lacked the ability to paraphrase sentences, as they simply select important
words without actually understanding their contexts nor understanding their
meaning, here comes the use of Deep Learning based architectures (Abstractive
Methods), which effectively tries to understand the meaning of sentences to
build meaningful summaries. In this work we discuss one of these new novel
approaches which combines curriculum learning with Deep Learning, this model is
called Scheduled Sampling. We apply this work to one of the most widely spoken
African languages which is the Amharic Language, as we try to enrich the
African NLP community with top-notch Deep Learning architectures.
| 2,020 | Computation and Language |
Semantic-based End-to-End Learning for Typhoon Intensity Prediction | Disaster prediction is one of the most critical tasks towards disaster
surveillance and preparedness. Existing technologies employ different machine
learning approaches to predict incoming disasters from historical environmental
data. However, for short-term disasters (e.g., earthquakes), historical data
alone has a limited prediction capability. Therefore, additional sources of
warnings are required for accurate prediction. We consider social media as a
supplementary source of knowledge in addition to historical environmental data.
However, social media posts (e.g., tweets) is very informal and contains only
limited content. To alleviate these limitations, we propose the combination of
semantically-enriched word embedding models to represent entities in tweets
with their semantic representations computed with the traditionalword2vec.
Moreover, we study how the correlation between social media posts and typhoons
magnitudes (also called intensities)-in terms of volume and sentiments of
tweets-. Based on these insights, we propose an end-to-end based framework that
learns from disaster-related tweets and environmental data to improve typhoon
intensity prediction. This paper is an extension of our work originally
published in K-CAP 2019 [32]. We extended this paper by building our framework
with state-of-the-art deep neural models, up-dated our dataset with new
typhoons and their tweets to-date and benchmark our approach against recent
baselines in disaster prediction. Our experimental results show that our
approach outperforms the accuracy of the state-of-the-art baselines in terms of
F1-score with (CNN by12.1%and BiLSTM by3.1%) improvement compared with last
experiments
| 2,020 | Computation and Language |
Span-based discontinuous constituency parsing: a family of exact
chart-based algorithms with time complexities from O(n^6) down to O(n^3) | We introduce a novel chart-based algorithm for span-based parsing of
discontinuous constituency trees of block degree two, including ill-nested
structures. In particular, we show that we can build variants of our parser
with smaller search spaces and time complexities ranging from $\mathcal O(n^6)$
down to $\mathcal O(n^3)$. The cubic time variant covers 98\% of constituents
observed in linguistic treebanks while having the same complexity as continuous
constituency parsers. We evaluate our approach on German and English treebanks
(Negra, Tiger and Discontinuous PTB) and report state-of-the-art results in the
fully supervised setting. We also experiment with pre-trained word embeddings
and \bert{}-based neural networks.
| 2,020 | Computation and Language |
The European Language Technology Landscape in 2020: Language-Centric and
Human-Centric AI for Cross-Cultural Communication in Multilingual Europe | Multilingualism is a cultural cornerstone of Europe and firmly anchored in
the European treaties including full language equality. However, language
barriers impacting business, cross-lingual and cross-cultural communication are
still omnipresent. Language Technologies (LTs) are a powerful means to break
down these barriers. While the last decade has seen various initiatives that
created a multitude of approaches and technologies tailored to Europe's
specific needs, there is still an immense level of fragmentation. At the same
time, AI has become an increasingly important concept in the European
Information and Communication Technology area. For a few years now, AI,
including many opportunities, synergies but also misconceptions, has been
overshadowing every other topic. We present an overview of the European LT
landscape, describing funding programmes, activities, actions and challenges in
the different countries with regard to LT, including the current state of play
in industry and the LT market. We present a brief overview of the main
LT-related activities on the EU level in the last ten years and develop
strategic guidance with regard to four key dimensions.
| 2,020 | Computation and Language |
Procedural Reading Comprehension with Attribute-Aware Context Flow | Procedural texts often describe processes (e.g., photosynthesis and cooking)
that happen over entities (e.g., light, food). In this paper, we introduce an
algorithm for procedural reading comprehension by translating the text into a
general formalism that represents processes as a sequence of transitions over
entity attributes (e.g., location, temperature). Leveraging pre-trained
language models, our model obtains entity-aware and attribute-aware
representations of the text by joint prediction of entity attributes and their
transitions. Our model dynamically obtains contextual encodings of the
procedural text exploiting information that is encoded about previous and
current states to predict the transition of a certain attribute which can be
identified as a span of text or from a pre-defined set of classes. Moreover,
our model achieves state of the art results on two procedural reading
comprehension datasets, namely ProPara and npn-cooking
| 2,020 | Computation and Language |
SPARQA: Skeleton-based Semantic Parsing for Complex Questions over
Knowledge Bases | Semantic parsing transforms a natural language question into a formal query
over a knowledge base. Many existing methods rely on syntactic parsing like
dependencies. However, the accuracy of producing such expressive formalisms is
not satisfying on long complex questions. In this paper, we propose a novel
skeleton grammar to represent the high-level structure of a complex question.
This dedicated coarse-grained formalism with a BERT-based parsing algorithm
helps to improve the accuracy of the downstream fine-grained semantic parsing.
Besides, to align the structure of a question with the structure of a knowledge
base, our multi-strategy method combines sentence-level and word-level
semantics. Our approach shows promising performance on several datasets.
| 2,020 | Computation and Language |
MULTEXT-East | MULTEXT-East language resources, a multilingual dataset for language
engineering research, focused on the morphosyntactic level of linguistic
description. The MULTEXT-East dataset includes the EAGLES-based morphosyntactic
specifications, morphosyntactic lexicons, and an annotated multilingual
corpora. The parallel corpus, the novel "1984" by George Orwell, is sentence
aligned and contains hand-validated morphosyntactic descriptions and lemmas.
The resources are uniformly encoded in XML, using the Text Encoding Initiative
Guidelines, TEI P5, and cover 16 languages: Bulgarian, Croatian, Czech,
English, Estonian, Hungarian, Macedonian, Persian, Polish, Resian, Romanian,
Russian, Serbian, Slovak, Slovene, and Ukrainian. This dataset is extensively
documented, and freely available for research purposes. This case study gives a
history of the development of the MULTEXT-East resources, presents their
encoding and components, discusses related work and gives some conclusions.
| 2,007 | Computation and Language |
Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent
Neural Networks | It is now established that modern neural language models can be successfully
trained on multiple languages simultaneously without changes to the underlying
architecture. But what kind of knowledge is really shared among languages
within these models? Does multilingual training mostly lead to an alignment of
the lexical representation spaces or does it also enable the sharing of purely
grammatical knowledge? In this paper we dissect different forms of
cross-lingual transfer and look for its most determining factors, using a
variety of models and probing tasks. We find that exposing our LMs to a related
language does not always increase grammatical knowledge in the target language,
and that optimal conditions for lexical-semantic transfer may not be optimal
for syntactic transfer.
| 2,021 | Computation and Language |
Appraisal Theories for Emotion Classification in Text | Automatic emotion categorization has been predominantly formulated as text
classification in which textual units are assigned to an emotion from a
predefined inventory, for instance following the fundamental emotion classes
proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert
Plutchik (adding trust, anticipation). This approach ignores existing
psychological theories to some degree, which provide explanations regarding the
perception of events. For instance, the description that somebody discovers a
snake is associated with fear, based on the appraisal as being an unpleasant
and non-controllable situation. This emotion reconstruction is even possible
without having access to explicit reports of a subjective feeling (for instance
expressing this with the words "I am afraid."). Automatic classification
approaches therefore need to learn properties of events as latent variables
(for instance that the uncertainty and the mental or physical effort associated
with the encounter of a snake leads to fear). With this paper, we propose to
make such interpretations of events explicit, following theories of cognitive
appraisal of events, and show their potential for emotion classification when
being encoded in classification models. Our results show that high quality
appraisal dimension assignments in event descriptions lead to an improvement in
the classification of discrete emotion categories. We make our corpus of
appraisal-annotated emotion-associated event descriptions publicly available.
| 2,020 | Computation and Language |
Inherent Dependency Displacement Bias of Transition-Based Algorithms | A wide variety of transition-based algorithms are currently used for
dependency parsers. Empirical studies have shown that performance varies across
different treebanks in such a way that one algorithm outperforms another on one
treebank and the reverse is true for a different treebank. There is often no
discernible reason for what causes one algorithm to be more suitable for a
certain treebank and less so for another. In this paper we shed some light on
this by introducing the concept of an algorithm's inherent dependency
displacement distribution. This characterises the bias of the algorithm in
terms of dependency displacement, which quantify both distance and direction of
syntactic relations. We show that the similarity of an algorithm's inherent
distribution to a treebank's displacement distribution is clearly correlated to
the algorithm's parsing performance on that treebank, specifically with highly
significant and substantial correlations for the predominant sentence lengths
in Universal Dependency treebanks. We also obtain results which show a more
discrete analysis of dependency displacement does not result in any meaningful
correlations.
| 2,020 | Computation and Language |
On the Integration of LinguisticFeatures into Statistical and Neural
Machine Translation | New machine translations (MT) technologies are emerging rapidly and with
them, bold claims of achieving human parity such as: (i) the results produced
approach "accuracy achieved by average bilingual human translators" (Wu et al.,
2017b) or (ii) the "translation quality is at human parity when compared to
professional human translators" (Hassan et al., 2018) have seen the light of
day (Laubli et al., 2018). Aside from the fact that many of these papers craft
their own definition of human parity, these sensational claims are often not
supported by a complete analysis of all aspects involved in translation.
Establishing the discrepancies between the strengths of statistical approaches
to MT and the way humans translate has been the starting point of our research.
By looking at MT output and linguistic theory, we were able to identify some
remaining issues. The problems range from simple number and gender agreement
errors to more complex phenomena such as the correct translation of aspectual
values and tenses. Our experiments confirm, along with other studies
(Bentivogli et al., 2016), that neural MT has surpassed statistical MT in many
aspects. However, some problems remain and others have emerged. We cover a
series of problems related to the integration of specific linguistic features
into statistical and neural MT, aiming to analyse and provide a solution to
some of them. Our work focuses on addressing three main research questions that
revolve around the complex relationship between linguistics and MT in general.
We identify linguistic information that is lacking in order for automatic
translation systems to produce more accurate translations and integrate
additional features into the existing pipelines. We identify overgeneralization
or 'algorithmic bias' as a potential drawback of neural MT and link it to many
of the remaining linguistic issues.
| 2,020 | Computation and Language |
Evaluating Amharic Machine Translation | Machine translation (MT) systems are now able to provide very accurate
results for high resource language pairs. However, for many low resource
languages, MT is still under active research. In this paper, we develop and
share a dataset to automatically evaluate the quality of MT systems for
Amharic. We compare two commercially available MT systems that support
translation of Amharic to and from English to assess the current state of MT
for Amharic. The BLEU score results show that the results for Amharic
translation are promising but still low. We hope that this dataset will be
useful to the research community both in academia and industry as a benchmark
to evaluate Amharic MT systems.
| 2,020 | Computation and Language |
Low Resource Neural Machine Translation: A Benchmark for Five African
Languages | Recent advents in Neural Machine Translation (NMT) have shown improvements in
low-resource language (LRL) translation tasks. In this work, we benchmark NMT
between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo,
Somali [SATOS]). We collected the available resources on the SATOS languages to
evaluate the current state of NMT for LRLs. Our evaluation, comparing a
baseline single language pair NMT model against semi-supervised learning,
transfer learning, and multilingual modeling, shows significant performance
improvements both in the En-LRL and LRL-En directions. In terms of averaged
BLEU score, the multilingual approach shows the largest gains, up to +5 points,
in six out of ten translation directions. To demonstrate the generalization
capability of each model, we also report results on multi-domain test sets. We
release the standardized experimental data and the test sets for future works
addressing the challenges of NMT in under-resourced settings, in particular for
the SATOS languages.
| 2,020 | Computation and Language |
Give your Text Representation Models some Love: the Case for Basque | Word embeddings and pre-trained language models allow to build rich
representations of text and have enabled improvements across most NLP tasks.
Unfortunately they are very expensive to train, and many small companies and
research groups tend to use models that have been pre-trained and made
available by third parties, rather than building their own. This is suboptimal
as, for many languages, the models have been trained on smaller (or lower
quality) corpora. In addition, monolingual pre-trained models for non-English
languages are not always available. At best, models for those languages are
included in multilingual versions, where each language shares the quota of
substrings and parameters with the rest of the languages. This is particularly
true for smaller languages such as Basque. In this paper we show that a number
of monolingual models (FastText word embeddings, FLAIR and BERT language
models) trained with larger Basque corpora produce much better results than
publicly available versions in downstream NLP tasks, including topic
classification, sentiment classification, PoS tagging and NER. This work sets a
new state-of-the-art in those tasks for Basque. All benchmarks and models used
in this work are publicly available.
| 2,020 | Computation and Language |
Multilingual Stance Detection: The Catalonia Independence Corpus | Stance detection aims to determine the attitude of a given text with respect
to a specific topic or claim. While stance detection has been fairly well
researched in the last years, most the work has been focused on English. This
is mainly due to the relative lack of annotated data in other languages. The
TW-10 Referendum Dataset released at IberEval 2018 is a previous effort to
provide multilingual stance-annotated data in Catalan and Spanish.
Unfortunately, the TW-10 Catalan subset is extremely imbalanced. This paper
addresses these issues by presenting a new multilingual dataset for stance
detection in Twitter for the Catalan and Spanish languages, with the aim of
facilitating research on stance detection in multilingual and cross-lingual
settings. The dataset is annotated with stance towards one topic, namely, the
independence of Catalonia. We also provide a semi-automatic method to annotate
the dataset based on a categorization of Twitter users. We experiment on the
new corpus with a number of supervised approaches, including linear classifiers
and deep learning methods. Comparison of our new corpus with the with the TW-1O
dataset shows both the benefits and potential of a well balanced corpus for
multilingual and cross-lingual research on stance detection. Finally, we
establish new state-of-the-art results on the TW-10 dataset, both for Catalan
and Spanish.
| 2,020 | Computation and Language |
Assessing Human Translations from French to Bambara for Machine
Learning: a Pilot Study | We present novel methods for assessing the quality of human-translated
aligned texts for learning machine translation models of under-resourced
languages. Malian university students translated French texts, producing either
written or oral translations to Bambara. Our results suggest that similar
quality can be obtained from either written or spoken translations for certain
kinds of texts. They also suggest specific instructions that human translators
should be given in order to improve the quality of their work.
| 2,020 | Computation and Language |
A Clustering Framework for Lexical Normalization of Roman Urdu | Roman Urdu is an informal form of the Urdu language written in Roman script,
which is widely used in South Asia for online textual content. It lacks
standard spelling and hence poses several normalization challenges during
automatic language processing. In this article, we present a feature-based
clustering framework for the lexical normalization of Roman Urdu corpora, which
includes a phonetic algorithm UrduPhone, a string matching component, a
feature-based similarity function, and a clustering algorithm Lex-Var.
UrduPhone encodes Roman Urdu strings to their pronunciation-based
representations. The string matching component handles character-level
variations that occur when writing Urdu using Roman script.
| 2,022 | Computation and Language |
Automatic Extraction of Bengali Root Verbs using Paninian Grammar | In this research work, we have proposed an algorithm based on supervised
learning methodology to extract the root forms of the Bengali verbs using the
grammatical rules proposed by Panini [1] in Ashtadhyayi. This methodology can
be applied for the languages which are derived from Sanskrit. The proposed
system has been developed based on tense, person and morphological inflections
of the verbs to find their root forms. The work has been executed in two
phases: first, the surface level forms or inflected forms of the verbs have
been classified into a certain number of groups of similar tense and person.
For this task, a standard pattern, available in Bengali language has been used.
Next, a set of rules have been applied to extract the root form from the
surface level forms of a verb. The system has been tested on 10000 verbs
collected from the Bengali text corpus developed in the TDIL project of the
Govt. of India. The accuracy of the output has been achieved 98% which is
verified by a linguistic expert. Root verb identification is a key step in
semantic searching, multi-sentence search query processing, understanding the
meaning of a language, disambiguation of word sense, classification of the
sentences etc.
| 2,020 | Computation and Language |
A Swiss German Dictionary: Variation in Speech and Writing | We introduce a dictionary containing forms of common words in various Swiss
German dialects normalized into High German. As Swiss German is, for now, a
predominantly spoken language, there is a significant variation in the written
forms, even between speakers of the same dialect. To alleviate the uncertainty
associated with this diversity, we complement the pairs of Swiss German - High
German words with the Swiss German phonetic transcriptions (SAMPA). This
dictionary becomes thus the first resource to combine large-scale spontaneous
translation with phonetic transcriptions. Moreover, we control for the regional
distribution and insure the equal representation of the major Swiss dialects.
The coupling of the phonetic and written Swiss German forms is powerful. We
show that they are sufficient to train a Transformer-based phoneme to grapheme
model that generates credible novel Swiss German writings. In addition, we show
that the inverse mapping - from graphemes to phonemes - can be modeled with a
transformer trained with the novel dictionary. This generation of
pronunciations for previously unknown words is key in training extensible
automated speech recognition (ASR) systems, which are key beneficiaries of this
dictionary.
| 2,020 | Computation and Language |
Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding | Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV, or CHV for
short), is a collection of medical terms written in plain English. It provides
a list of simple, easy, and clear terms that laymen prefer to use rather than
an equivalent professional medical term. The National Library of Medicine (NLM)
has integrated and mapped the CHV terms to their Unified Medical Language
System (UMLS). These CHV terms mapped to 56000 professional concepts on the
UMLS. We found that about 48% of these laymen's terms are still jargon and
matched with the professional terms on the UMLS. In this paper, we present an
enhanced word embedding technique that generates new CHV terms from a
consumer-generated text. We downloaded our corpus from a healthcare social
media and evaluated our new method based on iterative feedback to word
embedding using ground truth built from the existing CHV terms. Our feedback
algorithm outperformed unmodified GLoVe and new CHV terms have been detected.
| 2,020 | Computation and Language |
Adversarial Transfer Learning for Punctuation Restoration | Previous studies demonstrate that word embeddings and part-of-speech (POS)
tags are helpful for punctuation restoration tasks. However, two drawbacks
still exist. One is that word embeddings are pre-trained by unidirectional
language modeling objectives. Thus the word embeddings only contain
left-to-right context information. The other is that POS tags are provided by
an external POS tagger. So computation cost will be increased and incorrect
predicted tags may affect the performance of restoring punctuation marks during
decoding. This paper proposes adversarial transfer learning to address these
problems. A pre-trained bidirectional encoder representations from transformers
(BERT) model is used to initialize a punctuation model. Thus the transferred
model parameters carry both left-to-right and right-to-left representations.
Furthermore, adversarial multi-task learning is introduced to learn task
invariant knowledge for punctuation prediction. We use an extra POS tagging
task to help the training of the punctuation predicting task. Adversarial
training is utilized to prevent the shared parameters from containing task
specific information. We only use the punctuation predicting task to restore
marks during decoding stage. Therefore, it will not need extra computation and
not introduce incorrect tags from the POS tagger. Experiments are conducted on
IWSLT2011 datasets. The results demonstrate that the punctuation predicting
models obtain further performance improvement with task invariant knowledge
from the POS tagging task. Our best model outperforms the previous
state-of-the-art model trained only with lexical features by up to 9.2%
absolute overall F_1-score on test set.
| 2,020 | Computation and Language |
Comparative Analysis of N-gram Text Representation on Igbo Text Document
Similarity | The improvement in Information Technology has encouraged the use of Igbo in
the creation of text such as resources and news articles online. Text
similarity is of great importance in any text-based applications. This paper
presents a comparative analysis of n-gram text representation on Igbo text
document similarity. It adopted Euclidean similarity measure to determine the
similarities between Igbo text documents represented with two word-based n-gram
text representation (unigram and bigram) models. The evaluation of the
similarity measure is based on the adopted text representation models. The
model is designed with Object-Oriented Methodology and implemented with Python
programming language with tools from Natural Language Toolkits (NLTK). The
result shows that unigram represented text has highest distance values whereas
bigram has the lowest corresponding distance values. The lower the distance
value, the more similar the two documents and better the quality of the model
when used for a task that requires similarity measure. The similarity of two
documents increases as the distance value moves down to zero (0). Ideally, the
result analyzed revealed that Igbo text document similarity measured on bigram
represented text gives accurate similarity result. This will give better,
effective and accurate result when used for tasks such as text classification,
clustering and ranking on Igbo text.
| 2,017 | Computation and Language |
Unique Chinese Linguistic Phenomena | Linguistics holds unique characteristics of generality, stability, and
nationality, which will affect the formulation of extraction strategies and
should be incorporated into the relation extraction. Chinese open relation
extraction is not well-established, because of the complexity of Chinese
linguistics makes it harder to operate, and the methods for English are not
compatible with that for Chinese. The diversities between Chinese and English
linguistics are mainly reflected in morphology and syntax.
| 2,020 | Computation and Language |
Deep Learning Approach for Intelligent Named Entity Recognition of Cyber
Security | In recent years, the amount of Cyber Security data generated in the form of
unstructured texts, for example, social media resources, blogs, articles, and
so on has exceptionally increased. Named Entity Recognition (NER) is an initial
step towards converting this unstructured data into structured data which can
be used by a lot of applications. The existing methods on NER for Cyber
Security data are based on rules and linguistic characteristics. A Deep
Learning (DL) based approach embedded with Conditional Random Fields (CRFs) is
proposed in this paper. Several DL architectures are evaluated to find the most
optimal architecture. The combination of Bidirectional Gated Recurrent Unit
(Bi-GRU), Convolutional Neural Network (CNN), and CRF performed better compared
to various other DL frameworks on a publicly available benchmark dataset. This
may be due to the reason that the bidirectional structures preserve the
features related to the future and previous words in a sequence.
| 2,020 | Computation and Language |
Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter
Stream | In recent days, the amount of Cyber Security text data shared via social
media resources mainly Twitter has increased. An accurate analysis of this data
can help to develop cyber threat situational awareness framework for a cyber
threat. This work proposes a deep learning based approach for tweet data
analysis. To convert the tweets into numerical representations, various text
representations are employed. These features are feed into deep learning
architecture for optimal feature extraction as well as classification. Various
hyperparameter tuning approaches are used for identifying optimal text
representation method as well as optimal network parameters and network
structures for deep learning models. For comparative analysis, the classical
text representation method with classical machine learning algorithm is
employed. From the detailed analysis of experiments, we found that the deep
learning architecture with advanced text representation methods performed
better than the classical text representation and classical machine learning
algorithms. The primary reason for this is that the advanced text
representation methods have the capability to learn sequential properties which
exist among the textual data and deep learning architectures learns the optimal
features along with decreasing the feature size.
| 2,020 | Computation and Language |
Better Sign Language Translation with STMC-Transformer | Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR)
system to extract sign language glosses from videos. Then, a translation system
generates spoken language translations from the sign language glosses. This
paper focuses on the translation system and introduces the STMC-Transformer
which improves on the current state-of-the-art by over 5 and 7 BLEU
respectively on gloss-to-text and video-to-text translation of the
PHOENIX-Weather 2014T dataset. On the ASLG-PC12 corpus, we report an increase
of over 16 BLEU.
We also demonstrate the problem in current methods that rely on gloss
supervision. The video-to-text translation of our STMC-Transformer outperforms
translation of GT glosses. This contradicts previous claims that GT gloss
translation acts as an upper bound for SLT performance and reveals that glosses
are an inefficient representation of sign language. For future SLT research, we
therefore suggest an end-to-end training of the recognition and translation
models, or using a different sign language annotation scheme.
| 2,020 | Computation and Language |
Igbo-English Machine Translation: An Evaluation Benchmark | Although researchers and practitioners are pushing the boundaries and
enhancing the capacities of NLP tools and methods, works on African languages
are lagging. A lot of focus on well resourced languages such as English,
Japanese, German, French, Russian, Mandarin Chinese etc. Over 97% of the
world's 7000 languages, including African languages, are low resourced for NLP
i.e. they have little or no data, tools, and techniques for NLP research. For
instance, only 5 out of 2965, 0.19% authors of full text papers in the ACL
Anthology extracted from the 5 major conferences in 2018 ACL, NAACL, EMNLP,
COLING and CoNLL, are affiliated to African institutions. In this work, we
discuss our effort toward building a standard machine translation benchmark
dataset for Igbo, one of the 3 major Nigerian languages. Igbo is spoken by more
than 50 million people globally with over 50% of the speakers are in
southeastern Nigeria. Igbo is low resourced although there have been some
efforts toward developing IgboNLP such as part of speech tagging and diacritic
restoration
| 2,020 | Computation and Language |
Mapping Languages: The Corpus of Global Language Use | This paper describes a web-based corpus of global language use with a focus
on how this corpus can be used for data-driven language mapping. First, the
corpus provides a representation of where national varieties of major languages
are used (e.g., English, Arabic, Russian) together with consistently collected
data for each variety. Second, the paper evaluates a language identification
model that supports more local languages with smaller sample sizes than
alternative off-the-shelf models. Improved language identification is essential
for moving beyond majority languages. Given the focus on language mapping, the
paper analyzes how well this digital language data represents actual
populations by (i) systematically comparing the corpus with demographic
ground-truth data and (ii) triangulating the corpus with an alternate
Twitter-based dataset. In total, the corpus contains 423 billion words
representing 148 languages (with over 1 million words from each language) and
158 countries (again with over 1 million words from each country), all
distilled from Common Crawl web data. The main contribution of this paper, in
addition to describing this publicly-available corpus, is to provide a
comprehensive analysis of the relationship between two sources of digital data
(the web and Twitter) as well as their connection to underlying populations.
| 2,020 | Computation and Language |
Mapping Languages and Demographics with Georeferenced Corpora | This paper evaluates large georeferenced corpora, taken from both web-crawled
and social media sources, against ground-truth population and language-census
datasets. The goal is to determine (i) which dataset best represents population
demographics; (ii) in what parts of the world the datasets are most
representative of actual populations; and (iii) how to weight the datasets to
provide more accurate representations of underlying populations. The paper
finds that the two datasets represent very different populations and that they
correlate with actual populations with values of r=0.60 (social media) and
r=0.49 (web-crawled). Further, Twitter data makes better predictions about the
inventory of languages used in each country.
| 2,020 | Computation and Language |
How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability
in Context | We study the influence of context on sentence acceptability. First we compare
the acceptability ratings of sentences judged in isolation, with a relevant
context, and with an irrelevant context. Our results show that context induces
a cognitive load for humans, which compresses the distribution of ratings.
Moreover, in relevant contexts we observe a discourse coherence effect which
uniformly raises acceptability. Next, we test unidirectional and bidirectional
language models in their ability to predict acceptability ratings. The
bidirectional models show very promising results, with the best model achieving
a new state-of-the-art for unsupervised acceptability prediction. The two sets
of experiments provide insights into the cognitive aspects of sentence
processing and central issues in the computational modelling of text and
discourse.
| 2,020 | Computation and Language |
Understanding Linearity of Cross-Lingual Word Embedding Mappings | The technique of Cross-Lingual Word Embedding (CLWE) plays a fundamental role
in tackling Natural Language Processing challenges for low-resource languages.
Its dominant approaches assumed that the relationship between embeddings could
be represented by a linear mapping, but there has been no exploration of the
conditions under which this assumption holds. Such a research gap becomes very
critical recently, as it has been evidenced that relaxing mappings to be
non-linear can lead to better performance in some cases. We, for the first
time, present a theoretical analysis that identifies the preservation of
analogies encoded in monolingual word embeddings as a necessary and sufficient
condition for the ground-truth CLWE mapping between those embeddings to be
linear. On a novel cross-lingual analogy dataset that covers five
representative analogy categories for twelve distinct languages, we carry out
experiments which provide direct empirical support for our theoretical claim.
These results offer additional insight into the observations of other
researchers and contribute inspiration for the development of more effective
cross-lingual representation learning strategies.
| 2,022 | Computation and Language |
NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts | This paper introduces the first version of the NUBes corpus (Negation and
Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of
an on-going research and currently consists of 29,682 sentences obtained from
anonymised health records annotated with negation and uncertainty. The article
includes an exhaustive comparison with similar corpora in Spanish, and presents
the main annotation and design decisions. Additionally, we perform preliminary
experiments using deep learning algorithms to validate the annotated dataset.
As far as we know, NUBes is the largest publicly available corpus for negation
in Spanish and the first that also incorporates the annotation of speculation
cues, scopes, and events.
| 2,020 | Computation and Language |
Causal Inference of Script Knowledge | When does a sequence of events define an everyday scenario and how can this
knowledge be induced from text? Prior works in inducing such scripts have
relied on, in one form or another, measures of correlation between instances of
events in a corpus. We argue from both a conceptual and practical sense that a
purely correlation-based approach is insufficient, and instead propose an
approach to script induction based on the causal effect between events,
formally defined via interventions. Through both human and automatic
evaluations, we show that the output of our method based on causal effects
better matches the intuition of what a script represents
| 2,020 | Computation and Language |
R3: A Reading Comprehension Benchmark Requiring Reasoning Processes | Existing question answering systems can only predict answers without explicit
reasoning processes, which hinder their explainability and make us overestimate
their ability of understanding and reasoning over natural language. In this
work, we propose a novel task of reading comprehension, in which a model is
required to provide final answers and reasoning processes. To this end, we
introduce a formalism for reasoning over unstructured text, namely Text
Reasoning Meaning Representation (TRMR). TRMR consists of three phrases, which
is expressive enough to characterize the reasoning process to answer reading
comprehension questions. We develop an annotation platform to facilitate TRMR's
annotation, and release the R3 dataset, a \textbf{R}eading comprehension
benchmark \textbf{R}equiring \textbf{R}easoning processes. R3 contains over 60K
pairs of question-answer pairs and their TRMRs. Our dataset is available at:
\url{http://anonymous}.
| 2,020 | Computation and Language |
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing | Named entity typing (NET) is a classification task of assigning an entity
mention in the context with given semantic types. However, with the growing
size and granularity of the entity types, rare researches in previous concern
with newly emerged entity types. In this paper, we propose MZET, a novel memory
augmented FNET (Fine-grained NET) model, to tackle the unseen types in a
zero-shot manner. MZET incorporates character-level, word-level, and
contextural-level information to learn the entity mention representation.
Besides, MZET considers the semantic meaning and the hierarchical structure
into the entity type representation. Finally, through the memory component
which models the relationship between the entity mention and the entity type,
MZET transfer the knowledge from seen entity types to the zero-shot ones.
Extensive experiments on three public datasets show prominent performance
obtained by MZET, which surpasses the state-of-the-art FNET neural network
models with up to 7% gain in Micro-F1 and Macro-F1 score.
| 2,020 | Computation and Language |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training,
Understanding and Generation | In this paper, we introduce XGLUE, a new benchmark dataset that can be used
to train large-scale cross-lingual pre-trained models using multilingual and
bilingual corpora and evaluate their performance across a diverse set of
cross-lingual tasks. Comparing to GLUE(Wang et al., 2019), which is labeled in
English for natural language understanding tasks only, XGLUE has two main
advantages: (1) it provides 11 diversified tasks that cover both natural
language understanding and generation scenarios; (2) for each task, it provides
labeled data in multiple languages. We extend a recent cross-lingual
pre-trained model Unicoder(Huang et al., 2019) to cover both understanding and
generation tasks, which is evaluated on XGLUE as a strong baseline. We also
evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for
comparison.
| 2,020 | Computation and Language |
Learning synchronous context-free grammars with multiple specialised
non-terminals for hierarchical phrase-based translation | Translation models based on hierarchical phrase-based statistical machine
translation (HSMT) have shown better performances than the non-hierarchical
phrase-based counterparts for some language pairs. The standard approach to
HSMT learns and apply a synchronous context-free grammar with a single
non-terminal. The hypothesis behind the grammar refinement algorithm presented
in this work is that this single non-terminal is overloaded, and insufficiently
discriminative, and therefore, an adequate split of it into more specialised
symbols could lead to improved models. This paper presents a method to learn
synchronous context-free grammars with a huge number of initial non-terminals,
which are then grouped via a clustering algorithm. Our experiments show that
the resulting smaller set of non-terminals correctly capture the contextual
information that makes it possible to statistically significantly improve the
BLEU score of the standard HSMT approach.
| 2,020 | Computation and Language |
Finding Black Cat in a Coal Cellar -- Keyphrase Extraction &
Keyphrase-Rubric Relationship Classification from Complex Assignments | Diversity in content and open-ended questions are inherent in complex
assignments across online graduate programs. The natural scale of these
programs poses a variety of challenges across both peer and expert feedback
including rogue reviews. While the identification of relevant content and
associating it to predefined rubrics would simplify and improve the grading
process, the research to date is still in a nascent stage. As such in this
paper we aim to quantify the effectiveness of supervised and unsupervised
approaches for the task for keyphrase extraction and generic/specific
keyphrase-rubric relationship extraction. Through this study, we find that (i)
unsupervised MultiPartiteRank produces the best result for keyphrase extraction
(ii) supervised SVM classifier with BERT features that offer the best
performance for both generic and specific keyphrase-rubric relationship
classification. We finally present a comprehensive analysis and derive useful
observations for those interested in these tasks for the future. The source
code is released in \url{https://github.com/manikandan-ravikiran/cs6460-proj}.
| 2,020 | Computation and Language |
Analyzing autoencoder-based acoustic word embeddings | Recent studies have introduced methods for learning acoustic word embeddings
(AWEs)---fixed-size vector representations of words which encode their acoustic
features. Despite the widespread use of AWEs in speech processing research,
they have only been evaluated quantitatively in their ability to discriminate
between whole word tokens. To better understand the applications of AWEs in
various downstream tasks and in cognitive modeling, we need to analyze the
representation spaces of AWEs. Here we analyze basic properties of AWE spaces
learned by a sequence-to-sequence encoder-decoder model in six typologically
diverse languages. We first show that these AWEs preserve some information
about words' absolute duration and speaker. At the same time, the
representation space of these AWEs is organized such that the distance between
words' embeddings increases with those words' phonetic dissimilarity. Finally,
the AWEs exhibit a word onset bias, similar to patterns reported in various
studies on human speech processing and lexical access. We argue this is a
promising result and encourage further evaluation of AWEs as a potentially
useful tool in cognitive science, which could provide a link between speech
processing and lexical memory.
| 2,020 | Computation and Language |
Aligned Cross Entropy for Non-Autoregressive Machine Translation | Non-autoregressive machine translation models significantly speed up decoding
by allowing for parallel prediction of the entire target sequence. However,
modeling word order is more challenging due to the lack of autoregressive
factors in the model. This difficultly is compounded during training with cross
entropy loss, which can highly penalize small shifts in word order. In this
paper, we propose aligned cross entropy (AXE) as an alternative loss function
for training of non-autoregressive models. AXE uses a differentiable dynamic
program to assign loss based on the best possible monotonic alignment between
target tokens and model predictions. AXE-based training of conditional masked
language models (CMLMs) substantially improves performance on major WMT
benchmarks, while setting a new state of the art for non-autoregressive models.
| 2,020 | Computation and Language |
A Set of Recommendations for Assessing Human-Machine Parity in Language
Translation | The quality of machine translation has increased remarkably over the past
years, to the degree that it was found to be indistinguishable from
professional human translation in a number of empirical investigations. We
reassess Hassan et al.'s 2018 investigation into Chinese to English news
translation, showing that the finding of human-machine parity was owed to
weaknesses in the evaluation design - which is currently considered best
practice in the field. We show that the professional human translations
contained significantly fewer errors, and that perceived quality in human
evaluation depends on the choice of raters, the availability of linguistic
context, and the creation of reference translations. Our results call for
revisiting current best practices to assess strong machine translation systems
in general and human-machine parity in particular, for which we offer a set of
recommendations based on our empirical findings.
| 2,020 | Computation and Language |
Pre-training for Abstractive Document Summarization by Reinstating
Source Text | Abstractive document summarization is usually modeled as a
sequence-to-sequence (Seq2Seq) learning problem. Unfortunately, training large
Seq2Seq based summarization models on limited supervised summarization data is
challenging. This paper presents three pre-training objectives which allow us
to pre-train a Seq2Seq based abstractive summarization model on unlabeled text.
The main idea is that, given an input text artificially constructed from a
document, a model is pre-trained to reinstate the original document. These
objectives include sentence reordering, next sentence generation, and masked
document generation, which have close relations with the abstractive document
summarization task. Experiments on two benchmark summarization datasets (i.e.,
CNN/DailyMail and New York Times) show that all three objectives can improve
performance upon baselines. Compared to models pre-trained on large-scale data
(more than 160GB), our method, with only 19GB text for pre-training, achieves
comparable results, which demonstrates its effectiveness.
| 2,020 | Computation and Language |
News-Driven Stock Prediction With Attention-Based Noisy Recurrent State
Transition | We consider direct modeling of underlying stock value movement sequences over
time in the news-driven stock movement prediction. A recurrent state transition
model is constructed, which better captures a gradual process of stock movement
continuously by modeling the correlation between past and future price
movements. By separating the effects of news and noise, a noisy random factor
is also explicitly fitted based on the recurrent states. Results show that the
proposed model outperforms strong baselines. Thanks to the use of attention
over news events, our model is also more explainable. To our knowledge, we are
the first to explicitly model both events and noise over a fundamental stock
value state for news-driven stock movement prediction.
| 2,022 | Computation and Language |
CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot
Intent Detection | In this paper, we formulate a more realistic and difficult problem setup for
the intent detection task in natural language understanding, namely Generalized
Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a joint label
space consisting of both existing intents which have enough labeled data and
novel intents which only have a few examples for each class. To approach this
problem, we propose a novel model, Conditional Text Generation with BERT
(CG-BERT). CG-BERT effectively leverages a large pre-trained language model to
generate text conditioned on the intent label. By modeling the utterance
distribution with variational inference, CG-BERT can generate diverse
utterances for the novel intents even with only a few utterances available.
Experimental results show that CG-BERT achieves state-of-the-art performance on
the GFSID task with 1-shot and 5-shot settings on two real-world datasets.
| 2,020 | Computation and Language |
Evaluating Multimodal Representations on Visual Semantic Textual
Similarity | The combination of visual and textual representations has produced excellent
results in tasks such as image captioning and visual question answering, but
the inference capabilities of multimodal representations are largely untested.
In the case of textual representations, inference tasks such as Textual
Entailment and Semantic Textual Similarity have been often used to benchmark
the quality of textual representations. The long term goal of our research is
to devise multimodal representation techniques that improve current inference
capabilities. We thus present a novel task, Visual Semantic Textual Similarity
(vSTS), where such inference ability can be tested directly. Given two items
comprised each by an image and its accompanying caption, vSTS systems need to
assess the degree to which the captions in context are semantically equivalent
to each other. Our experiments using simple multimodal representations show
that the addition of image representations produces better inference, compared
to text-only representations. The improvement is observed both when directly
computing the similarity between the representations of the two items, and when
learning a siamese network based on vSTS training data. Our work shows, for the
first time, the successful contribution of visual information to textual
inference, with ample room for benchmarking more complex multimodal
representation options.
| 2,020 | Computation and Language |
Knowledge Guided Metric Learning for Few-Shot Text Classification | The training of deep-learning-based text classification models relies heavily
on a huge amount of annotation data, which is difficult to obtain. When the
labeled data is scarce, models tend to struggle to achieve satisfactory
performance. However, human beings can distinguish new categories very
efficiently with few examples. This is mainly due to the fact that human beings
can leverage knowledge obtained from relevant tasks. Inspired by human
intelligence, we propose to introduce external knowledge into few-shot learning
to imitate human knowledge. A novel parameter generator network is investigated
to this end, which is able to use the external knowledge to generate relation
network parameters. Metrics can be transferred among tasks when equipped with
these generated parameters, so that similar tasks use similar metrics while
different tasks use different metrics. Through experiments, we demonstrate that
our method outperforms the state-of-the-art few-shot text classification
models.
| 2,020 | Computation and Language |
Conversational Question Reformulation via Sequence-to-Sequence
Architectures and Pretrained Language Models | This paper presents an empirical study of conversational question
reformulation (CQR) with sequence-to-sequence architectures and pretrained
language models (PLMs). We leverage PLMs to address the strong token-to-token
independence assumption made in the common objective, maximum likelihood
estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue
systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset
as an in-domain task and validate the models using data from the TREC 2019 CAsT
Track as an out-domain task. Examining a variety of architectures with
different numbers of parameters, we demonstrate that the recent text-to-text
transfer transformer (T5) achieves the best results both on CANARD and CAsT
with fewer parameters, compared to similar transformer architectures.
| 2,020 | Computation and Language |
Benchmarking Machine Reading Comprehension: A Psychological Perspective | Machine reading comprehension (MRC) has received considerable attention as a
benchmark for natural language understanding. However, the conventional task
design of MRC lacks explainability beyond the model interpretation, i.e.,
reading comprehension by a model cannot be explained in human terms. To this
end, this position paper provides a theoretical basis for the design of MRC
datasets based on psychology as well as psychometrics, and summarizes it in
terms of the prerequisites for benchmarking MRC. We conclude that future
datasets should (i) evaluate the capability of the model for constructing a
coherent and grounded representation to understand context-dependent situations
and (ii) ensure substantive validity by shortcut-proof questions and
explanation as a part of the task design.
| 2,021 | Computation and Language |
"None of the Above":Measure Uncertainty in Dialog Response Retrieval | This paper discusses the importance of uncovering uncertainty in end-to-end
dialog tasks, and presents our experimental results on uncertainty
classification on the Ubuntu Dialog Corpus. We show that, instead of retraining
models for this specific purpose, the original retrieval model's underlying
confidence concerning the best prediction can be captured with trivial
additional computation.
| 2,020 | Computation and Language |
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment
Analysis | Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect
term extraction, opinion term extraction, and aspect-level sentiment
classification, which are typically handled in a separate or joint manner.
However, previous approaches do not well exploit the interactive relations
among three subtasks and do not pertinently leverage the easily available
document-level labeled domain/sentiment knowledge, which restricts their
performances. To address these issues, we propose a novel Iterative
Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. For one thing,
through the interactive correlations between the ABSA subtasks, our IMKTN
transfers the task-specific knowledge from any two of the three subtasks to
another one at the token level by utilizing a well-designed routing algorithm,
that is, any two of the three subtasks will help the third one. For another,
our IMKTN pertinently transfers the document-level knowledge, i.e.,
domain-specific and sentiment-related knowledge, to the aspect-level subtasks
to further enhance the corresponding performance. Experimental results on three
benchmark datasets demonstrate the effectiveness and superiority of our
approach.
| 2,021 | Computation and Language |
Pre-Trained and Attention-Based Neural Networks for Building Noetic
Task-Oriented Dialogue Systems | The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology
Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new
elements that are vital for the creation of a deployed task-oriented dialogue
system. This paper describes our systems that are evaluated on all subtasks
under this challenge. We study the problem of employing pre-trained
attention-based network for multi-turn dialogue systems. Meanwhile, several
adaptation methods are proposed to adapt the pre-trained language models for
multi-turn dialogue systems, in order to keep the intrinsic property of
dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our
proposed models ranked fourth in subtask 1, third in subtask 2, and first in
subtask 3 and subtask 4 respectively.
| 2,020 | Computation and Language |
A Dependency Syntactic Knowledge Augmented Interactive Architecture for
End-to-End Aspect-based Sentiment Analysis | The aspect-based sentiment analysis (ABSA) task remains to be a long-standing
challenge, which aims to extract the aspect term and then identify its
sentiment orientation.In previous approaches, the explicit syntactic structure
of a sentence, which reflects the syntax properties of natural language and
hence is intuitively crucial for aspect term extraction and sentiment
recognition, is typically neglected or insufficiently modeled. In this paper,
we thus propose a novel dependency syntactic knowledge augmented interactive
architecture with multi-task learning for end-to-end ABSA. This model is
capable of fully exploiting the syntactic knowledge (dependency relations and
types) by leveraging a well-designed Dependency Relation Embedded Graph
Convolutional Network (DreGcn). Additionally, we design a simple yet effective
message-passing mechanism to ensure that our model learns from multiple related
tasks in a multi-task learning framework. Extensive experimental results on
three benchmark datasets demonstrate the effectiveness of our approach, which
significantly outperforms existing state-of-the-art methods. Besides, we
achieve further improvements by using BERT as an additional feature extractor.
| 2,020 | Computation and Language |
BAE: BERT-based Adversarial Examples for Text Classification | Modern text classification models are susceptible to adversarial examples,
perturbed versions of the original text indiscernible by humans which get
misclassified by the model. Recent works in NLP use rule-based synonym
replacement strategies to generate adversarial examples. These strategies can
lead to out-of-context and unnaturally complex token replacements, which are
easily identifiable by humans. We present BAE, a black box attack for
generating adversarial examples using contextual perturbations from a BERT
masked language model. BAE replaces and inserts tokens in the original text by
masking a portion of the text and leveraging the BERT-MLM to generate
alternatives for the masked tokens. Through automatic and human evaluations, we
show that BAE performs a stronger attack, in addition to generating adversarial
examples with improved grammaticality and semantic coherence as compared to
prior work.
| 2,022 | Computation and Language |
Learning a Simple and Effective Model for Multi-turn Response Generation
with Auxiliary Tasks | We study multi-turn response generation for open-domain dialogues. The
existing state-of-the-art addresses the problem with deep neural architectures.
While these models improved response quality, their complexity also hinders the
application of the models in real systems. In this work, we pursue a model that
has a simple structure yet can effectively leverage conversation contexts for
response generation. To this end, we propose four auxiliary tasks including
word order recovery, utterance order recovery, masked word recovery, and masked
utterance recovery, and optimize the objectives of these tasks together with
maximizing the likelihood of generation. By this means, the auxiliary tasks
that relate to context understanding can guide the learning of the generation
model to achieve a better local optimum. Empirical studies with three
benchmarks indicate that our model can significantly outperform
state-of-the-art generation models in terms of response quality on both
automatic evaluation and human judgment, and at the same time enjoys a much
faster decoding process.
| 2,020 | Computation and Language |
Hooks in the Headline: Learning to Generate Headlines with Controlled
Styles | Current summarization systems only produce plain, factual headlines, but do
not meet the practical needs of creating memorable titles to increase exposure.
We propose a new task, Stylistic Headline Generation (SHG), to enrich the
headlines with three style options (humor, romance and clickbait), in order to
attract more readers. With no style-specific article-headline pair (only a
standard headline summarization dataset and mono-style corpora), our method
TitleStylist generates style-specific headlines by combining the summarization
and reconstruction tasks into a multitasking framework. We also introduced a
novel parameter sharing scheme to further disentangle the style from the text.
Through both automatic and human evaluation, we demonstrate that TitleStylist
can generate relevant, fluent headlines with three target styles: humor,
romance, and clickbait. The attraction score of our model generated headlines
surpasses that of the state-of-the-art summarization model by 9.68%, and even
outperforms human-written references.
| 2,020 | Computation and Language |
Open Domain Dialogue Generation with Latent Images | We consider grounding open domain dialogues with images. Existing work
assumes that both an image and a textual context are available, but
image-grounded dialogues by nature are more difficult to obtain than textual
dialogues. Thus, we propose learning a response generation model with both
image-grounded dialogues and textual dialogues by assuming that the visual
scene information at the time of a conversation can be represented by an image,
and trying to recover the latent images of the textual dialogues through
text-to-image generation techniques. The likelihood of the two types of
dialogues is then formulated by a response generator and an image reconstructor
that are learned within a conditional variational auto-encoding framework.
Empirical studies are conducted in both image-grounded conversation and
text-based conversation. In the first scenario, image-grounded dialogues,
especially under a low-resource setting, can be effectively augmented by
textual dialogues with latent images; while in the second scenario, latent
images can enrich the content of responses and at the same time keep them
relevant to contexts.
| 2,021 | Computation and Language |
Graph Sequential Network for Reasoning over Sequences | Recently Graph Neural Network (GNN) has been applied successfully to various
NLP tasks that require reasoning, such as multi-hop machine reading
comprehension. In this paper, we consider a novel case where reasoning is
needed over graphs built from sequences, i.e. graph nodes with sequence data.
Existing GNN models fulfill this goal by first summarizing the node sequences
into fixed-dimensional vectors, then applying GNN on these vectors. To avoid
information loss inherent in the early summarization and make sequential
labeling tasks on GNN output feasible, we propose a new type of GNN called
Graph Sequential Network (GSN), which features a new message passing algorithm
based on co-attention between a node and each of its neighbors. We validate the
proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on
HotpotQA and graph based fact verification on FEVER. Both tasks require
reasoning over multiple documents or sentences. Our experimental results show
that the proposed GSN attains better performance than the standard GNN based
methods.
| 2,020 | Computation and Language |
Talk to Papers: Bringing Neural Question Answering to Academic Search | We introduce Talk to Papers, which exploits the recent open-domain question
answering (QA) techniques to improve the current experience of academic search.
It's designed to enable researchers to use natural language queries to find
precise answers and extract insights from a massive amount of academic papers.
We present a large improvement over classic search engine baseline on several
standard QA datasets and provide the community a collaborative data collection
tool to curate the first natural language processing research QA dataset via a
community effort.
| 2,020 | Computation and Language |
Generating Hierarchical Explanations on Text Classification via Feature
Interaction Detection | Generating explanations for neural networks has become crucial for their
applications in real-world with respect to reliability and trustworthiness. In
natural language processing, existing methods usually provide important
features which are words or phrases selected from an input text as an
explanation, but ignore the interactions between them. It poses challenges for
humans to interpret an explanation and connect it to model prediction. In this
work, we build hierarchical explanations by detecting feature interactions.
Such explanations visualize how words and phrases are combined at different
levels of the hierarchy, which can help users understand the decision-making of
black-box models. The proposed method is evaluated with three neural text
classifiers (LSTM, CNN, and BERT) on two benchmark datasets, via both automatic
and human evaluations. Experiments show the effectiveness of the proposed
method in providing explanations that are both faithful to models and
interpretable to humans.
| 2,020 | Computation and Language |
A Hierarchical Network for Abstractive Meeting Summarization with
Cross-Domain Pretraining | With the abundance of automatic meeting transcripts, meeting summarization is
of great interest to both participants and other parties. Traditional methods
of summarizing meetings depend on complex multi-step pipelines that make joint
optimization intractable. Meanwhile, there are a handful of deep neural models
for text summarization and dialogue systems. However, the semantic structure
and styles of meeting transcripts are quite different from articles and
conversations. In this paper, we propose a novel abstractive summary network
that adapts to the meeting scenario. We design a hierarchical structure to
accommodate long meeting transcripts and a role vector to depict the difference
among speakers. Furthermore, due to the inadequacy of meeting summary data, we
pretrain the model on large-scale news summary data. Empirical results show
that our model outperforms previous approaches in both automatic metrics and
human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases
from 34.66% to 46.28%.
| 2,020 | Computation and Language |
Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised
Neural Machine Translation | We explore ways of incorporating bilingual dictionaries to enable
semi-supervised neural machine translation. Conventional back-translation
methods have shown success in leveraging target side monolingual data. However,
since the quality of back-translation models is tied to the size of the
available parallel corpora, this could adversely impact the synthetically
generated sentences in a low resource setting. We propose a simple data
augmentation technique to address both this shortcoming. We incorporate widely
available bilingual dictionaries that yield word-by-word translations to
generate synthetic sentences. This automatically expands the vocabulary of the
model while maintaining high quality content. Our method shows an appreciable
improvement in performance over strong baselines.
| 2,020 | Computation and Language |
Machine Translation Pre-training for Data-to-Text Generation -- A Case
Study in Czech | While there is a large body of research studying deep learning methods for
text generation from structured data, almost all of it focuses purely on
English. In this paper, we study the effectiveness of machine translation based
pre-training for data-to-text generation in non-English languages. Since the
structured data is generally expressed in English, text generation into other
languages involves elements of translation, transliteration and copying -
elements already encoded in neural machine translation systems. Moreover, since
data-to-text corpora are typically small, this task can benefit greatly from
pre-training. Based on our experiments on Czech, a morphologically complex
language, we find that pre-training lets us train end-to-end models with
significantly improved performance, as judged by automatic metrics and human
evaluation. We also show that this approach enjoys several desirable
properties, including improved performance in low data scenarios and robustness
to unseen slot values.
| 2,020 | Computation and Language |
A Resource for Studying Chatino Verbal Morphology | We present the first resource focusing on the verbal inflectional morphology
of San Juan Quiahije Chatino, a tonal mesoamerican language spoken in Mexico.
We provide a collection of complete inflection tables of 198 lemmata, with
morphological tags based on the UniMorph schema. We also provide baseline
results on three core NLP tasks: morphological analysis, lemmatization, and
morphological inflection.
| 2,020 | Computation and Language |
Unsupervised Domain Clusters in Pretrained Language Models | The notion of "in-domain data" in NLP is often over-simplistic and vague, as
textual data varies in many nuanced linguistic aspects such as topic, style or
level of formality. In addition, domain labels are many times unavailable,
making it challenging to build domain-specific systems. We show that massive
pre-trained language models implicitly learn sentence representations that
cluster by domains without supervision -- suggesting a simple data-driven
definition of domains in textual data. We harness this property and propose
domain data selection methods based on such models, which require only a small
set of in-domain monolingual data. We evaluate our data selection methods for
neural machine translation across five diverse domains, where they outperform
an established approach as measured by both BLEU and by precision and recall of
sentence selection with respect to an oracle.
| 2,020 | Computation and Language |
GIANT: Scalable Creation of a Web-scale Ontology | Understanding what online users may pay attention to is key to content
recommendation and search services. These services will benefit from a highly
structured and web-scale ontology of entities, concepts, events, topics and
categories. While existing knowledge bases and taxonomies embody a large volume
of entities and categories, we argue that they fail to discover properly
grained concepts, events and topics in the language style of online population.
Neither is a logically structured ontology maintained among these notions. In
this paper, we present GIANT, a mechanism to construct a user-centered,
web-scale, structured ontology, containing a large number of natural language
phrases conforming to user attentions at various granularities, mined from a
vast volume of web documents and search click graphs. Various types of edges
are also constructed to maintain a hierarchy in the ontology. We present our
graph-neural-network-based techniques used in GIANT, and evaluate the proposed
methods as compared to a variety of baselines. GIANT has produced the Attention
Ontology, which has been deployed in various Tencent applications involving
over a billion users. Online A/B testing performed on Tencent QQ Browser shows
that Attention Ontology can significantly improve click-through rates in news
recommendation.
| 2,020 | Computation and Language |
Reference Language based Unsupervised Neural Machine Translation | Exploiting a common language as an auxiliary for better translation has a
long tradition in machine translation and lets supervised learning-based
machine translation enjoy the enhancement delivered by the well-used pivot
language in the absence of a source language to target language parallel
corpus. The rise of unsupervised neural machine translation (UNMT) almost
completely relieves the parallel corpus curse, though UNMT is still subject to
unsatisfactory performance due to the vagueness of the clues available for its
core back-translation training. Further enriching the idea of pivot translation
by extending the use of parallel corpora beyond the source-target paradigm, we
propose a new reference language-based framework for UNMT, RUNMT, in which the
reference language only shares a parallel corpus with the source, but this
corpus still indicates a signal clear enough to help the reconstruction
training of UNMT through a proposed reference agreement mechanism. Experimental
results show that our methods improve the quality of UNMT over that of a strong
baseline that uses only one auxiliary language, demonstrating the usefulness of
the proposed reference language-based UNMT and establishing a good start for
the community.
| 2,020 | Computation and Language |
Reinforced Multi-task Approach for Multi-hop Question Generation | Question generation (QG) attempts to solve the inverse of question answering
(QA) problem by generating a natural language question given a document and an
answer. While sequence to sequence neural models surpass rule-based systems for
QG, they are limited in their capacity to focus on more than one supporting
fact. For QG, we often require multiple supporting facts to generate
high-quality questions. Inspired by recent works on multi-hop reasoning in QA,
we take up Multi-hop question generation, which aims at generating relevant
questions based on supporting facts in the context. We employ multitask
learning with the auxiliary task of answer-aware supporting fact prediction to
guide the question generator. In addition, we also proposed a question-aware
reward function in a Reinforcement Learning (RL) framework to maximize the
utilization of the supporting facts. We demonstrate the effectiveness of our
approach through experiments on the multi-hop question answering dataset,
HotPotQA. Empirical evaluation shows our model to outperform the single-hop
neural question generation models on both automatic evaluation metrics such as
BLEU, METEOR, and ROUGE, and human evaluation metrics for quality and coverage
of the generated questions.
| 2,020 | Computation and Language |
FastBERT: a Self-distilling BERT with Adaptive Inference Time | Pre-trained language models like BERT have proven to be highly performant.
However, they are often computationally expensive in many practical scenarios,
for such heavy models can hardly be readily implemented with limited resources.
To improve their efficiency with an assured model performance, we propose a
novel speed-tunable FastBERT with adaptive inference time. The speed at
inference can be flexibly adjusted under varying demands, while redundant
calculation of samples is avoided. Moreover, this model adopts a unique
self-distillation mechanism at fine-tuning, further enabling a greater
computational efficacy with minimal loss in performance. Our model achieves
promising results in twelve English and Chinese datasets. It is able to speed
up by a wide range from 1 to 12 times than BERT if given different speedup
thresholds to make a speed-performance tradeoff.
| 2,020 | Computation and Language |
Detecting and Understanding Generalization Barriers for Neural Machine
Translation | Generalization to unseen instances is our eternal pursuit for all data-driven
models. However, for realistic task like machine translation, the traditional
approach measuring generalization in an average sense provides poor
understanding for the fine-grained generalization ability. As a remedy, this
paper attempts to identify and understand generalization barrier words within
an unseen input sentence that \textit{cause} the degradation of fine-grained
generalization. We propose a principled definition of generalization barrier
words and a modified version which is tractable in computation. Based on the
modified one, we propose three simple methods for barrier detection by the
search-aware risk estimation through counterfactual generation. We then conduct
extensive analyses on those detected generalization barrier words on both
Zh$\Leftrightarrow$En NIST benchmarks from various perspectives. Potential
usage of the detected barrier words is also discussed.
| 2,020 | Computation and Language |
Arabic Offensive Language on Twitter: Analysis and Experiments | Detecting offensive language on Twitter has many applications ranging from
detecting/predicting bullying to measuring polarization. In this paper, we
focus on building a large Arabic offensive tweet dataset. We introduce a method
for building a dataset that is not biased by topic, dialect, or target. We
produce the largest Arabic dataset to date with special tags for vulgarity and
hate speech. We thoroughly analyze the dataset to determine which topics,
dialects, and gender are most associated with offensive tweets and how Arabic
speakers use offensive language. Lastly, we conduct many experiments to produce
strong results (F1 = 83.2) on the dataset using SOTA techniques.
| 2,021 | Computation and Language |
AR: Auto-Repair the Synthetic Data for Neural Machine Translation | Compared with only using limited authentic parallel data as training corpus,
many studies have proved that incorporating synthetic parallel data, which
generated by back translation (BT) or forward translation (FT, or
selftraining), into the NMT training process can significantly improve
translation quality. However, as a well-known shortcoming, synthetic parallel
data is noisy because they are generated by an imperfect NMT system. As a
result, the improvements in translation quality bring by the synthetic parallel
data are greatly diminished. In this paper, we propose a novel Auto- Repair
(AR) framework to improve the quality of synthetic data. Our proposed AR model
can learn the transformation from low quality (noisy) input sentence to high
quality sentence based on large scale monolingual data with BT and FT
techniques. The noise in synthetic parallel data will be sufficiently
eliminated by the proposed AR model and then the repaired synthetic parallel
data can help the NMT models to achieve larger improvements. Experimental
results show that our approach can effective improve the quality of synthetic
parallel data and the NMT model with the repaired synthetic data achieves
consistent improvements on both WMT14 EN!DE and IWSLT14 DE!EN translation
tasks.
| 2,020 | Computation and Language |
Understanding Learning Dynamics for Neural Machine Translation | Despite the great success of NMT, there still remains a severe challenge: it
is hard to interpret the internal dynamics during its training process. In this
paper we propose to understand learning dynamics of NMT by using a recent
proposed technique named Loss Change Allocation
(LCA)~\citep{lan-2019-loss-change-allocation}. As LCA requires calculating the
gradient on an entire dataset for each update, we instead present an
approximate to put it into practice in NMT scenario. %motivated by the lesson
from sgd. Our simulated experiment shows that such approximate calculation is
efficient and is empirically proved to deliver consistent results to the
brute-force implementation. In particular, extensive experiments on two
standard translation benchmark datasets reveal some valuable findings.
| 2,020 | Computation and Language |
Stylistic Dialogue Generation via Information-Guided Reinforcement
Learning Strategy | Stylistic response generation is crucial for building an engaging dialogue
system for industrial use. While it has attracted much research interest,
existing methods often generate stylistic responses at the cost of the content
quality (relevance and fluency). To enable better balance between the content
quality and the style, we introduce a new training strategy, know as
Information-Guided Reinforcement Learning (IG-RL). In IG-RL, a training model
is encouraged to explore stylistic expressions while being constrained to
maintain its content quality. This is achieved by adopting reinforcement
learning strategy with statistical style information guidance for
quality-preserving explorations. Experiments on two datasets show that the
proposed approach outperforms several strong baselines in terms of the overall
response performance.
| 2,020 | Computation and Language |
Syntax-driven Iterative Expansion Language Models for Controllable Text
Generation | The dominant language modeling paradigm handles text as a sequence of
discrete tokens. While that approach can capture the latent structure of the
text, it is inherently constrained to sequential dynamics for text generation.
We propose a new paradigm for introducing a syntactic inductive bias into
neural text generation, where the dependency parse tree is used to drive the
Transformer model to generate sentences iteratively.
Our experiments show that this paradigm is effective at text generation, with
quality between LSTMs and Transformers, and comparable diversity, requiring
less than half their decoding steps, and its generation process allows direct
control over the syntactic constructions of the generated text, enabling the
induction of stylistic variations.
| 2,020 | Computation and Language |
Prototype-to-Style: Dialogue Generation with Style-Aware Editing on
Retrieval Memory | The ability of a dialog system to express prespecified language style during
conversations has a direct, positive impact on its usability and on user
satisfaction. We introduce a new prototype-to-style (PS) framework to tackle
the challenge of stylistic dialogue generation. The framework uses an
Information Retrieval (IR) system and extracts a response prototype from the
retrieved response. A stylistic response generator then takes the prototype and
the desired language style as model input to obtain a high-quality and
stylistic response. To effectively train the proposed model, we propose a new
style-aware learning objective as well as a de-noising learning strategy.
Results on three benchmark datasets from two languages demonstrate that the
proposed approach significantly outperforms existing baselines in both
in-domain and cross-domain evaluations
| 2,020 | Computation and Language |
Speaker Recognition using SincNet and X-Vector Fusion | In this paper, we propose an innovative approach to perform speaker
recognition by fusing two recently introduced deep neural networks (DNNs)
namely - SincNet and X-Vector. The idea behind using SincNet filters on the raw
speech waveform is to extract more distinguishing frequency-related features in
the initial convolution layers of the CNN architecture. X-Vectors are used to
take advantage of the fact that this embedding is an efficient method to churn
out fixed dimension features from variable length speech utterances, something
which is challenging in plain CNN techniques, making it efficient both in terms
of speed and accuracy. Our approach uses the best of both worlds by combining
X-vector in the later layers while using SincNet filters in the initial layers
of our deep model. This approach allows the network to learn better embedding
and converge quicker. Previous works use either X-Vector or SincNet Filters or
some modifications, however we introduce a novel fusion architecture wherein we
have combined both the techniques to gather more information about the speech
signal hence, giving us better results. Our method focuses on the VoxCeleb1
dataset for speaker recognition, and we have used it for both training and
testing purposes.
| 2,020 | Computation and Language |
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence
Tokens on Text Generation with GPT2 | The semantics of a text is manifested not only by what is read, but also by
what is not read. In this article, we will study how the implicit "not read"
information such as end-of-paragraph (\eop) and end-of-sequence (\eos) affect
the quality of text generation. Specifically, we find that the pre-trained
language model GPT2 can generate better continuations by learning to generate
the \eop in the fine-tuning stage. Experimental results on English story
generation show that \eop can lead to higher BLEU score and lower \eos
perplexity. We also conduct experiments on a self-collected Chinese essay
dataset with Chinese-GPT2, a character level LM without \eop or \eos during
pre-training. Experimental results show that the Chinese GPT2 can generate
better essay endings with \eop.
| 2,021 | Computation and Language |
Natural language processing for word sense disambiguation and
information extraction | This research work deals with Natural Language Processing (NLP) and
extraction of essential information in an explicit form. The most common among
the information management strategies is Document Retrieval (DR) and
Information Filtering. DR systems may work as combine harvesters, which bring
back useful material from the vast fields of raw material. With large amount of
potentially useful information in hand, an Information Extraction (IE) system
can then transform the raw material by refining and reducing it to a germ of
original text. A Document Retrieval system collects the relevant documents
carrying the required information, from the repository of texts. An IE system
then transforms them into information that is more readily digested and
analyzed. It isolates relevant text fragments, extracts relevant information
from the fragments, and then arranges together the targeted information in a
coherent framework. The thesis presents a new approach for Word Sense
Disambiguation using thesaurus. The illustrative examples supports the
effectiveness of this approach for speedy and effective disambiguation. A
Document Retrieval method, based on Fuzzy Logic has been described and its
application is illustrated. A question-answering system describes the operation
of information extraction from the retrieved text documents. The process of
information extraction for answering a query is considerably simplified by
using a Structured Description Language (SDL) which is based on cardinals of
queries in the form of who, what, when, where and why. The thesis concludes
with the presentation of a novel strategy based on Dempster-Shafer theory of
evidential reasoning, for document retrieval and information extraction. This
strategy permits relaxation of many limitations, which are inherent in Bayesian
probabilistic approach.
| 2,020 | Computation and Language |
Hierarchical Entity Typing via Multi-level Learning to Rank | We propose a novel method for hierarchical entity classification that
embraces ontological structure at both training and during prediction. At
training, our novel multi-level learning-to-rank loss compares positive types
against negative siblings according to the type tree. During prediction, we
define a coarse-to-fine decoder that restricts viable candidates at each level
of the ontology based on already predicted parent type(s). We achieve
state-of-the-art across multiple datasets, particularly with respect to strict
accuracy.
| 2,020 | Computation and Language |
Continual Domain-Tuning for Pretrained Language Models | Pre-trained language models (LM) such as BERT, DistilBERT, and RoBERTa can be
tuned for different domains (domain-tuning) by continuing the pre-training
phase on a new target domain corpus. This simple domain tuning (SDT) technique
has been widely used to create domain-tuned models such as BioBERT, SciBERT and
ClinicalBERT. However, during the pretraining phase on the target domain, the
LM models may catastrophically forget the patterns learned from their source
domain. In this work, we study the effects of catastrophic forgetting on
domain-tuned LM models and investigate methods that mitigate its negative
effects. We propose continual learning (CL) based alternatives for SDT, that
aim to reduce catastrophic forgetting. We show that these methods may increase
the performance of LM models on downstream target domain tasks. Additionally,
we also show that constraining the LM model from forgetting the source domain
leads to downstream task models that are more robust to domain shifts. We
analyze the computational cost of using our proposed CL methods and provide
recommendations for computationally lightweight and effective CL domain-tuning
procedures.
| 2,021 | Computation and Language |
Finding the Optimal Vocabulary Size for Neural Machine Translation | We cast neural machine translation (NMT) as a classification task in an
autoregressive setting and analyze the limitations of both classification and
autoregression components. Classifiers are known to perform better with
balanced class distributions during training. Since the Zipfian nature of
languages causes imbalanced classes, we explore its effect on NMT. We analyze
the effect of various vocabulary sizes on NMT performance on multiple languages
with many data sizes, and reveal an explanation for why certain vocabulary
sizes are better than others.
| 2,021 | Computation and Language |
Domain-based Latent Personal Analysis and its use for impersonation
detection in social media | Zipf's law defines an inverse proportion between a word's ranking in a given
corpus and its frequency in it, roughly dividing the vocabulary into frequent
words and infrequent ones. Here, we stipulate that within a domain an author's
signature can be derived from, in loose terms, the author's missing popular
words and frequently used infrequent-words. We devise a method, termed Latent
Personal Analysis (LPA), for finding domain-based attributes for entities in a
domain: their distance from the domain and their signature, which determines
how they most differ from a domain. We identify the most suitable distance
metric for the method among several and construct the distances and personal
signatures for authors, the domain's entities. The signature consists of both
over-used terms (compared to the average), and missing popular terms. We
validate the correctness and power of the signatures in identifying users and
set existence conditions. We then show uses for the method in explainable
authorship attribution: we define algorithms that utilize LPA to identify two
types of impersonation in social media: (1) authors with sockpuppets (multiple)
accounts; (2) front users accounts, operated by several authors. We validate
the algorithms and employ them over a large scale dataset obtained from a
social media site with over 4000 users. We corroborate these results using
temporal rate analysis. LPA can further be used to devise personal attributes
in a wide range of scientific domains in which the constituents have a
long-tail distribution of elements.
| 2,021 | Computation and Language |
Exploring Early Prediction of Buyer-Seller Negotiation Outcomes | Agents that negotiate with humans find broad applications in pedagogy and
conversational AI. Most efforts in human-agent negotiations rely on restrictive
menu-driven interfaces for communication. To advance the research in
language-based negotiation systems, we explore a novel task of early prediction
of buyer-seller negotiation outcomes, by varying the fraction of utterances
that the model can access. We explore the feasibility of early prediction by
using traditional feature-based methods, as well as by incorporating the
non-linguistic task context into a pretrained language model using sentence
templates. We further quantify the extent to which linguistic features help in
making better predictions apart from the task-specific price information.
Finally, probing the pretrained model helps us to identify specific features,
such as trust and agreement, that contribute to the prediction performance.
| 2,021 | Computation and Language |
Learning to Recover Reasoning Chains for Multi-Hop Question Answering
via Cooperative Games | We propose the new problem of learning to recover reasoning chains from
weakly supervised signals, i.e., the question-answer pairs. We propose a
cooperative game approach to deal with this problem, in which how the evidence
passages are selected and how the selected passages are connected are handled
by two models that cooperate to select the most confident chains from a large
set of candidates (from distant supervision). For evaluation, we created
benchmarks based on two multi-hop QA datasets, HotpotQA and MedHop; and
hand-labeled reasoning chains for the latter. The experimental results
demonstrate the effectiveness of our proposed approach.
| 2,020 | Computation and Language |
PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative
Dialogue Systems | Open-domain generative dialogue systems have attracted considerable attention
over the past few years. Currently, how to automatically evaluate them, is
still a big challenge problem. As far as we know, there are three kinds of
automatic methods to evaluate the open-domain generative dialogue systems: (1)
Word-overlap-based metrics; (2) Embedding-based metrics; (3) Learning-based
metrics. Due to the lack of systematic comparison, it is not clear which kind
of metrics are more effective. In this paper, we will first measure
systematically all kinds of automatic evaluation metrics over the same
experimental setting to check which kind is best. Through extensive
experiments, the learning-based metrics are demonstrated that they are the most
effective evaluation metrics for open-domain generative dialogue systems.
Moreover, we observe that nearly all learning-based metrics depend on the
negative sampling mechanism, which obtains an extremely imbalanced and
low-quality dataset to train a score model. In order to address this issue, we
propose a novel and feasible learning-based metric that can significantly
improve the correlation with human judgments by using augmented POsitive
samples and valuable NEgative samples, called PONE. Extensive experiments
demonstrate that our proposed evaluation method significantly outperforms the
state-of-the-art learning-based evaluation methods, with an average correlation
improvement of 13.18%. In addition, we have publicly released the codes of our
proposed method and state-of-the-art baselines.
| 2,020 | Computation and Language |
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