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A Word-Complexity Lexicon and A Neural Readability Ranking Model for
Lexical Simplification | Current lexical simplification approaches rely heavily on heuristics and
corpus level features that do not always align with human judgment. We create a
human-rated word-complexity lexicon of 15,000 English words and propose a novel
neural readability ranking model with a Gaussian-based feature vectorization
layer that utilizes these human ratings to measure the complexity of any given
word or phrase. Our model performs better than the state-of-the-art systems for
different lexical simplification tasks and evaluation datasets. Additionally,
we also produce SimplePPDB++, a lexical resource of over 10 million simplifying
paraphrase rules, by applying our model to the Paraphrase Database (PPDB).
| 2,018 | Computation and Language |
Mixture of Expert/Imitator Networks: Scalable Semi-supervised Learning
Framework | The current success of deep neural networks (DNNs) in an increasingly broad
range of tasks involving artificial intelligence strongly depends on the
quality and quantity of labeled training data. In general, the scarcity of
labeled data, which is often observed in many natural language processing
tasks, is one of the most important issues to be addressed. Semi-supervised
learning (SSL) is a promising approach to overcoming this issue by
incorporating a large amount of unlabeled data. In this paper, we propose a
novel scalable method of SSL for text classification tasks. The unique property
of our method, Mixture of Expert/Imitator Networks, is that imitator networks
learn to "imitate" the estimated label distribution of the expert network over
the unlabeled data, which potentially contributes a set of features for the
classification. Our experiments demonstrate that the proposed method
consistently improves the performance of several types of baseline DNNs. We
also demonstrate that our method has the more data, better performance property
with promising scalability to the amount of unlabeled data.
| 2,018 | Computation and Language |
An Empirical Study on Crosslingual Transfer in Probabilistic Topic
Models | Probabilistic topic modeling is a popular choice as the first step of
crosslingual tasks to enable knowledge transfer and extract multilingual
features. While many multilingual topic models have been developed, their
assumptions on the training corpus are quite varied, and it is not clear how
well the models can be applied under various training conditions. In this
paper, we systematically study the knowledge transfer mechanisms behind
different multilingual topic models, and through a broad set of experiments
with four models on ten languages, we provide empirical insights that can
inform the selection and future development of multilingual topic models.
| 2,019 | Computation and Language |
BLEU is Not Suitable for the Evaluation of Text Simplification | BLEU is widely considered to be an informative metric for text-to-text
generation, including Text Simplification (TS). TS includes both lexical and
structural aspects. In this paper we show that BLEU is not suitable for the
evaluation of sentence splitting, the major structural simplification
operation. We manually compiled a sentence splitting gold standard corpus
containing multiple structural paraphrases, and performed a correlation
analysis with human judgments. We find low or no correlation between BLEU and
the grammaticality and meaning preservation parameters where sentence splitting
is involved. Moreover, BLEU often negatively correlates with simplicity,
essentially penalizing simpler sentences.
| 2,018 | Computation and Language |
Robust Neural Abstractive Summarization Systems and Evaluation against
Adversarial Information | Sequence-to-sequence (seq2seq) neural models have been actively investigated
for abstractive summarization. Nevertheless, existing neural abstractive
systems frequently generate factually incorrect summaries and are vulnerable to
adversarial information, suggesting a crucial lack of semantic understanding.
In this paper, we propose a novel semantic-aware neural abstractive
summarization model that learns to generate high quality summaries through
semantic interpretation over salient content. A novel evaluation scheme with
adversarial samples is introduced to measure how well a model identifies
off-topic information, where our model yields significantly better performance
than the popular pointer-generator summarizer. Human evaluation also confirms
that our system summaries are uniformly more informative and faithful as well
as less redundant than the seq2seq model.
| 2,018 | Computation and Language |
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared
Reconstruction Mechanism | Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. Recently, Wang et al. (2018) proposed a novel
reconstruction-based approach to alleviating dropped pronoun (DP) translation
problems for neural machine translation models. In this work, we improve the
original model from two perspectives. First, we employ a shared reconstructor
to better exploit encoder and decoder representations. Second, we jointly learn
to translate and predict DPs in an end-to-end manner, to avoid the errors
propagated from an external DP prediction model. Experimental results show that
our approach significantly improves both translation performance and DP
prediction accuracy.
| 2,018 | Computation and Language |
UMONS Submission for WMT18 Multimodal Translation Task | This paper describes the UMONS solution for the Multimodal Machine
Translation Task presented at the third conference on machine translation
(WMT18). We explore a novel architecture, called deepGRU, based on recent
findings in the related task of Neural Image Captioning (NIC). The models
presented in the following sections lead to the best METEOR translation score
for both constrained (English, image) -> German and (English, image) -> French
sub-tasks.
| 2,018 | Computation and Language |
Bringing back simplicity and lightliness into neural image captioning | Neural Image Captioning (NIC) or neural caption generation has attracted a
lot of attention over the last few years. Describing an image with a natural
language has been an emerging challenge in both fields of computer vision and
language processing. Therefore a lot of research has focused on driving this
task forward with new creative ideas. So far, the goal has been to maximize
scores on automated metric and to do so, one has to come up with a plurality of
new modules and techniques. Once these add up, the models become complex and
resource-hungry. In this paper, we take a small step backwards in order to
study an architecture with interesting trade-off between performance and
computational complexity. To do so, we tackle every component of a neural
captioning model and propose one or more solution that lightens the model
overall. Our ideas are inspired by two related tasks: Multimodal and Monomodal
Neural Machine Translation.
| 2,018 | Computation and Language |
Improving Topic Models with Latent Feature Word Representations | Probabilistic topic models are widely used to discover latent topics in
document collections, while latent feature vector representations of words have
been used to obtain high performance in many NLP tasks. In this paper, we
extend two different Dirichlet multinomial topic models by incorporating latent
feature vector representations of words trained on very large corpora to
improve the word-topic mapping learnt on a smaller corpus. Experimental results
show that by using information from the external corpora, our new models
produce significant improvements on topic coherence, document clustering and
document classification tasks, especially on datasets with few or short
documents.
| 2,015 | Computation and Language |
(Self-Attentive) Autoencoder-based Universal Language Representation for
Machine Translation | Universal language representation is the holy grail in machine translation
(MT). Thanks to the new neural MT approach, it seems that there are good
perspectives towards this goal. In this paper, we propose a new architecture
based on combining variational autoencoders with encoder-decoders and
introducing an interlingual loss as an additional training objective. By adding
and forcing this interlingual loss, we are able to train multiple encoders and
decoders for each language, sharing a common universal representation. Since
the final objective of this universal representation is producing close results
for similar input sentences (in any language), we propose to evaluate it by
encoding the same sentence in two different languages, decoding both latent
representations into the same language and comparing both outputs. Preliminary
results on the WMT 2017 Turkish/English task shows that the proposed
architecture is capable of learning a universal language representation and
simultaneously training both translation directions with state-of-the-art
results.
| 2,018 | Computation and Language |
Neural Adaptation Layers for Cross-domain Named Entity Recognition | Recent research efforts have shown that neural architectures can be effective
in conventional information extraction tasks such as named entity recognition,
yielding state-of-the-art results on standard newswire datasets. However,
despite significant resources required for training such models, the
performance of a model trained on one domain typically degrades dramatically
when applied to a different domain, yet extracting entities from new emerging
domains such as social media can be of significant interest. In this paper, we
empirically investigate effective methods for conveniently adapting an
existing, well-trained neural NER model for a new domain. Unlike existing
approaches, we propose lightweight yet effective methods for performing domain
adaptation for neural models. Specifically, we introduce adaptation layers on
top of existing neural architectures, where no re-training using the source
domain data is required. We conduct extensive empirical studies and show that
our approach significantly outperforms state-of-the-art methods.
| 2,018 | Computation and Language |
Structured Content Preservation for Unsupervised Text Style Transfer | Text style transfer aims to modify the style of a sentence while keeping its
content unchanged. Recent style transfer systems often fail to faithfully
preserve the content after changing the style. This paper proposes a structured
content preserving model that leverages linguistic information in the
structured fine-grained supervisions to better preserve the style-independent
content during style transfer. In particular, we achieve the goal by devising
rich model objectives based on both the sentence's lexical information and a
language model that conditions on content. The resulting model therefore is
encouraged to retain the semantic meaning of the target sentences. We perform
extensive experiments that compare our model to other existing approaches in
the tasks of sentiment and political slant transfer. Our model achieves
significant improvement in terms of both content preservation and style
transfer in automatic and human evaluation.
| 2,018 | Computation and Language |
Poincar\'e GloVe: Hyperbolic Word Embeddings | Words are not created equal. In fact, they form an aristocratic graph with a
latent hierarchical structure that the next generation of unsupervised learned
word embeddings should reveal. In this paper, justified by the notion of
delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in
a Cartesian product of hyperbolic spaces which we theoretically connect to the
Gaussian word embeddings and their Fisher geometry. This connection allows us
to introduce a novel principled hypernymy score for word embeddings. Moreover,
we adapt the well-known Glove algorithm to learn unsupervised word embeddings
in this type of Riemannian manifolds. We further explain how to solve the
analogy task using the Riemannian parallel transport that generalizes vector
arithmetics to this new type of geometry. Empirically, based on extensive
experiments, we prove that our embeddings, trained unsupervised, are the first
to simultaneously outperform strong and popular baselines on the tasks of
similarity, analogy and hypernymy detection. In particular, for word hypernymy,
we obtain new state-of-the-art on fully unsupervised WBLESS classification
accuracy.
| 2,018 | Computation and Language |
Diacritization of Maghrebi Arabic Sub-Dialects | Diacritization process attempt to restore the short vowels in Arabic written
text; which typically are omitted. This process is essential for applications
such as Text-to-Speech (TTS). While diacritization of Modern Standard Arabic
(MSA) still holds the lion share, research on dialectal Arabic (DA)
diacritization is very limited. In this paper, we present our contribution and
results on the automatic diacritization of two sub-dialects of Maghrebi Arabic,
namely Tunisian and Moroccan, using a character-level deep neural network
architecture that stacks two bi-LSTM layers over a CRF output layer. The model
achieves word error rate of 2.7% and 3.6% for Moroccan and Tunisian
respectively and is capable of implicitly identifying the sub-dialect of the
input.
| 2,019 | Computation and Language |
Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model
Refinement for a Low Resource Indian Language | We address the problem of efficient acoustic-model refinement (continuous
retraining) using semi-supervised and active learning for a low resource Indian
language, wherein the low resource constraints are having i) a small labeled
corpus from which to train a baseline `seed' acoustic model and ii) a large
training corpus without orthographic labeling or from which to perform a data
selection for manual labeling at low costs. The proposed semi-supervised
learning decodes the unlabeled large training corpus using the seed model and
through various protocols, selects the decoded utterances with high reliability
using confidence levels (that correlate to the WER of the decoded utterances)
and iterative bootstrapping. The proposed active learning protocol uses
confidence level based metric to select the decoded utterances from the large
unlabeled corpus for further labeling. The semi-supervised learning protocols
can offer a WER reduction, from a poorly trained seed model, by as much as 50%
of the best WER-reduction realizable from the seed model's WER, if the large
corpus were labeled and used for acoustic-model training. The active learning
protocols allow that only 60% of the entire training corpus be manually
labeled, to reach the same performance as the entire data.
| 2,018 | Computation and Language |
U-Net: Machine Reading Comprehension with Unanswerable Questions | Machine reading comprehension with unanswerable questions is a new
challenging task for natural language processing. A key subtask is to reliably
predict whether the question is unanswerable. In this paper, we propose a
unified model, called U-Net, with three important components: answer pointer,
no-answer pointer, and answer verifier. We introduce a universal node and thus
process the question and its context passage as a single contiguous sequence of
tokens. The universal node encodes the fused information from both the question
and passage, and plays an important role to predict whether the question is
answerable and also greatly improves the conciseness of the U-Net. Different
from the state-of-art pipeline models, U-Net can be learned in an end-to-end
fashion. The experimental results on the SQuAD 2.0 dataset show that U-Net can
effectively predict the unanswerability of questions and achieves an F1 score
of 71.7 on SQuAD 2.0.
| 2,018 | Computation and Language |
A Machine Learning Approach to Persian Text Readability Assessment Using
a Crowdsourced Dataset | An automated approach to text readability assessment is essential to a
language and can be a powerful tool for improving the understandability of
texts written and published in that language. However, the Persian language,
which is spoken by over 110 million speakers, lacks such a system. Unlike other
languages such as English, French, and Chinese, very limited research studies
have been carried out to build an accurate and reliable text readability
assessment system for the Persian language. In the present research, the first
Persian dataset for text readability assessment was gathered and the first
model for Persian text readability assessment using machine learning was
introduced. The experiments showed that this model was accurate and could
assess the readability of Persian texts with a high degree of confidence. The
results of this study can be used in a number of applications such as medical
and educational text readability evaluation and have the potential to be the
cornerstone of future studies in Persian text readability assessment.
| 2,020 | Computation and Language |
Adversarial Text Generation Without Reinforcement Learning | Generative Adversarial Networks (GANs) have experienced a recent surge in
popularity, performing competitively in a variety of tasks, especially in
computer vision. However, GAN training has shown limited success in natural
language processing. This is largely because sequences of text are discrete,
and thus gradients cannot propagate from the discriminator to the generator.
Recent solutions use reinforcement learning to propagate approximate gradients
to the generator, but this is inefficient to train. We propose to utilize an
autoencoder to learn a low-dimensional representation of sentences. A GAN is
then trained to generate its own vectors in this space, which decode to
realistic utterances. We report both random and interpolated samples from the
generator. Visualization of sentence vectors indicate our model correctly
learns the latent space of the autoencoder. Both human ratings and BLEU scores
show that our model generates realistic text against competitive baselines.
| 2,019 | Computation and Language |
An Instance Transfer based Approach Using Enhanced Recurrent Neural
Network for Domain Named Entity Recognition | Recently, neural networks have shown promising results for named entity
recognition (NER), which needs a number of labeled data to for model training.
When meeting a new domain (target domain) for NER, there is no or a few labeled
data, which makes domain NER much more difficult. As NER has been researched
for a long time, some similar domain already has well labelled data (source
domain). Therefore, in this paper, we focus on domain NER by studying how to
utilize the labelled data from such similar source domain for the new target
domain. We design a kernel function based instance transfer strategy by getting
similar labelled sentences from a source domain. Moreover, we propose an
enhanced recurrent neural network (ERNN) by adding an additional layer that
combines the source domain labelled data into traditional RNN structure.
Comprehensive experiments are conducted on two datasets. The comparison results
among HMM, CRF and RNN show that RNN performs bette than others. When there is
no labelled data in domain target, compared to directly using the source domain
labelled data without selecting transferred instances, our enhanced RNN
approach gets improvement from 0.8052 to 0.9328 in terms of F1 measure.
| 2,018 | Computation and Language |
Using Sentiment Representation Learning to Enhance Gender Classification
for User Profiling | User profiling means exploiting the technology of machine learning to predict
attributes of users, such as demographic attributes, hobby attributes,
preference attributes, etc. It's a powerful data support of precision
marketing. Existing methods mainly study network behavior, personal
preferences, post texts to build user profile. Through our data analysis of
micro-blog, we find that females show more positive and have richer emotions
than males in online social platform. This difference is very conducive to the
distinction between genders. Therefore, we argue that sentiment context is
important as well for user profiling.This paper focuses on exploiting microblog
user posts to predict one of the demographic labels: gender. We propose a
Sentiment Representation Learning based Multi-Layer Perceptron(SRL-MLP) model
to classify gender. First we build a sentiment polarity classifier in advance
by training Long Short-Term Memory(LSTM) model on e-commerce review corpus.
Next we transfer sentiment representation to a basic MLP network. Last we
conduct experiments on gender classification by sentiment representation.
Experimental results show that our approach can improve gender classification
accuracy by 5.53\%, from 84.20\% to 89.73\%.
| 2,018 | Computation and Language |
FlowQA: Grasping Flow in History for Conversational Machine
Comprehension | Conversational machine comprehension requires the understanding of the
conversation history, such as previous question/answer pairs, the document
context, and the current question. To enable traditional, single-turn models to
encode the history comprehensively, we introduce Flow, a mechanism that can
incorporate intermediate representations generated during the process of
answering previous questions, through an alternating parallel processing
structure. Compared to approaches that concatenate previous questions/answers
as input, Flow integrates the latent semantics of the conversation history more
deeply. Our model, FlowQA, shows superior performance on two recently proposed
conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The
effectiveness of Flow also shows in other tasks. By reducing sequential
instruction understanding to conversational machine comprehension, FlowQA
outperforms the best models on all three domains in SCONE, with +1.8% to +4.4%
improvement in accuracy.
| 2,019 | Computation and Language |
Word Embeddings from Large-Scale Greek Web Content | Word embeddings are undoubtedly very useful components in many NLP tasks. In
this paper, we present word embeddings and other linguistic resources trained
on the largest to date digital Greek language corpus. We also present a live
web tool for testing the Greek word embeddings, by offering "analogy",
"similarity score" and "most similar words" functions. Through our explorer,
one could interact with the Greek word vectors.
| 2,018 | Computation and Language |
Exploring the Use of Attention within an Neural Machine Translation
Decoder States to Translate Idioms | Idioms pose problems to almost all Machine Translation systems. This type of
language is very frequent in day-to-day language use and cannot be simply
ignored. The recent interest in memory augmented models in the field of
Language Modelling has aided the systems to achieve good results by bridging
long-distance dependencies. In this paper we explore the use of such techniques
into a Neural Machine Translation system to help in translation of idiomatic
language.
| 2,018 | Computation and Language |
Robust Neural Machine Translation with Joint Textual and Phonetic
Embedding | Neural machine translation (NMT) is notoriously sensitive to noises, but
noises are almost inevitable in practice. One special kind of noise is the
homophone noise, where words are replaced by other words with similar
pronunciations. We propose to improve the robustness of NMT to homophone noises
by 1) jointly embedding both textual and phonetic information of source
sentences, and 2) augmenting the training dataset with homophone noises.
Interestingly, to achieve better translation quality and more robustness, we
found that most (though not all) weights should be put on the phonetic rather
than textual information. Experiments show that our method not only
significantly improves the robustness of NMT to homophone noises, but also
surprisingly improves the translation quality on some clean test sets.
| 2,019 | Computation and Language |
Marrying Universal Dependencies and Universal Morphology | The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects
each present schemata for annotating the morphosyntactic details of language.
Each project also provides corpora of annotated text in many languages - UD at
the token level and UniMorph at the type level. As each corpus is built by
different annotators, language-specific decisions hinder the goal of universal
schemata. With compatibility of tags, each project's annotations could be used
to validate the other's. Additionally, the availability of both type- and
token-level resources would be a boon to tasks such as parsing and homograph
disambiguation. To ease this interoperability, we present a deterministic
mapping from Universal Dependencies v2 features into the UniMorph schema. We
validate our approach by lookup in the UniMorph corpora and find a
macro-average of 64.13% recall. We also note incompatibilities due to paucity
of data on either side. Finally, we present a critical evaluation of the
foundations, strengths, and weaknesses of the two annotation projects.
| 2,018 | Computation and Language |
Can Euroscepticism Contribute to a European Public Sphere? The
Europeanization of Media Discourses about Euroscepticism across Six Countries | This study compares the media discourses about Euroscepticism in 2014 across
six countries (United Kingdom, Ireland, France, Spain, Sweden, and Denmark). We
assess the extent to which the mass media's reporting of Euroscepticism
indicates the Europeanization of public spheres. Using a mixed-methods approach
combining LDA topic modeling and qualitative coding, we find that approximately
70 per cent of print articles mentioning "Euroscepticism" or "Eurosceptic" are
framed in a non-domestic (i.e. European) context. In five of the six cases
studied, articles exhibiting a European context are strikingly similar in
content, with the British case as the exception. However, coverage of British
Euroscepticism drives Europeanization in other Member States. Bivariate
logistic regressions further reveal three macro-level structural variables that
significantly correlate with a Europeanized media discourse: newspaper type
(tabloid or broadsheet), presence of a strong Eurosceptic party, and
relationship to the EU budget (net contributor or receiver of EU funds).
| 2,018 | Computation and Language |
Named Entity Analysis and Extraction with Uncommon Words | Most previous research treats named entity extraction and classification as
an end-to-end task. We argue that the two sub-tasks should be addressed
separately. Entity extraction lies at the level of syntactic analysis while
entity classification lies at the level of semantic analysis. According to Noam
Chomsky's "Syntactic Structures," pp. 93-94 (Chomsky 1957), syntax is not
appealed to semantics and semantics does not affect syntax. We analyze two
benchmark datasets for the characteristics of named entities, finding that
uncommon words can distinguish named entities from common text; where uncommon
words are the words that hardly appear in common text and they are mainly the
proper nouns. Experiments validate that lexical and syntactic features achieve
state-of-the-art performance on entity extraction and that semantic features do
not further improve the extraction performance, in both of our model and the
state-of-the-art baselines. With Chomsky's view, we also explain the failure of
joint syntactic and semantic parsings in other works.
| 2,018 | Computation and Language |
Multi-Source Neural Machine Translation with Data Augmentation | Multi-source translation systems translate from multiple languages to a
single target language. By using information from these multiple sources, these
systems achieve large gains in accuracy. To train these systems, it is
necessary to have corpora with parallel text in multiple sources and the target
language. However, these corpora are rarely complete in practice due to the
difficulty of providing human translations in all of the relevant languages. In
this paper, we propose a data augmentation approach to fill such incomplete
parts using multi-source neural machine translation (NMT). In our experiments,
results varied over different language combinations but significant gains were
observed when using a source language similar to the target language.
| 2,018 | Computation and Language |
Creating a New Persian Poet Based on Machine Learning | In this article we describe an application of Machine Learning (ML) and
Linguistic Modeling to generate persian poems. In fact we teach machine by
reading and learning persian poems to generate fake poems in the same style of
the original poems. As two well known poets we used Hafez (1310-1390) and Saadi
(1210-1292) poems. First we feed the machine with Hafez poems to generate fake
poems with the same style and then we feed the machine with the both Hafez and
Saadi poems to generate a new style poems which is combination of these two
poets styles with emotional (Hafez) and rational (Saadi) elements. This idea of
combination of different styles with ML opens new gates for extending the
treasure of past literature of different cultures. Results show with enough
memory, processing power and time it is possible to generate reasonable good
poems.
| 2,018 | Computation and Language |
Neural Morphological Tagging for Estonian | We develop neural morphological tagging and disambiguation models for
Estonian. First, we experiment with two neural architectures for morphological
tagging - a standard multiclass classifier which treats each morphological tag
as a single unit, and a sequence model which handles the morphological tags as
sequences of morphological category values. Secondly, we complement these
models with the analyses generated by a rule-based Estonian morphological
analyser (MA) VABAMORF , thus performing a soft morphological disambiguation.
We compare two ways of supplementing a neural morphological tagger with the MA
outputs: firstly, by adding the combined analyses embeddings to the word
representation input to the neural tagging model, and secondly, by adopting an
attention mechanism to focus on the most relevant analyses generated by the MA.
Experiments on three Estonian datasets show that our neural architectures
consistently outperform the non-neural baselines, including HMM-disambiguated
VABAMORF, while augmenting models with MA outputs results in a further
performance boost for both models.
| 2,018 | Computation and Language |
INFODENS: An Open-source Framework for Learning Text Representations | The advent of representation learning methods enabled large performance gains
on various language tasks, alleviating the need for manual feature engineering.
While engineered representations are usually based on some linguistic
understanding and are therefore more interpretable, learned representations are
harder to interpret. Empirically studying the complementarity of both
approaches can provide more linguistic insights that would help reach a better
compromise between interpretability and performance. We present INFODENS, a
framework for studying learned and engineered representations of text in the
context of text classification tasks. It is designed to simplify the tasks of
feature engineering as well as provide the groundwork for extracting learned
features and combining both approaches. INFODENS is flexible, extensible, with
a short learning curve, and is easy to integrate with many of the available and
widely used natural language processing tools.
| 2,018 | Computation and Language |
The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological
Reinflection | The CoNLL--SIGMORPHON 2018 shared task on supervised learning of
morphological generation featured data sets from 103 typologically diverse
languages. Apart from extending the number of languages involved in earlier
supervised tasks of generating inflected forms, this year the shared task also
featured a new second task which asked participants to inflect words in
sentential context, similar to a cloze task. This second task featured seven
languages. Task 1 received 27 submissions and task 2 received 6 submissions.
Both tasks featured a low, medium, and high data condition. Nearly all
submissions featured a neural component and built on highly-ranked systems from
the earlier 2017 shared task. In the inflection task (task 1), 41 of the 52
languages present in last year's inflection task showed improvement by the best
systems in the low-resource setting. The cloze task (task 2) proved to be
difficult, and few submissions managed to consistently improve upon both a
simple neural baseline system and a lemma-repeating baseline.
| 2,020 | Computation and Language |
Subword Semantic Hashing for Intent Classification on Small Datasets | In this paper, we introduce the use of Semantic Hashing as embedding for the
task of Intent Classification and achieve state-of-the-art performance on three
frequently used benchmarks. Intent Classification on a small dataset is a
challenging task for data-hungry state-of-the-art Deep Learning based systems.
Semantic Hashing is an attempt to overcome such a challenge and learn robust
text classification. Current word embedding based are dependent on
vocabularies. One of the major drawbacks of such methods is out-of-vocabulary
terms, especially when having small training datasets and using a wider
vocabulary. This is the case in Intent Classification for chatbots, where
typically small datasets are extracted from internet communication. Two
problems arise by the use of internet communication. First, such datasets miss
a lot of terms in the vocabulary to use word embeddings efficiently. Second,
users frequently make spelling errors. Typically, the models for intent
classification are not trained with spelling errors and it is difficult to
think about ways in which users will make mistakes. Models depending on a word
vocabulary will always face such issues. An ideal classifier should handle
spelling errors inherently. With Semantic Hashing, we overcome these challenges
and achieve state-of-the-art results on three datasets: AskUbuntu, Chatbot, and
Web Application. Our benchmarks are available online:
https://github.com/kumar-shridhar/Know-Your-Intent
| 2,020 | Computation and Language |
Strategies for Language Identification in Code-Mixed Low Resource
Languages | In recent years, substantial work has been done on language tagging of
code-mixed data, but most of them use large amounts of data to build their
models. In this article, we present three strategies to build a word level
language tagger for code-mixed data using very low resources. Each of them
secured an accuracy higher than our baseline model, and the best performing
system got an accuracy around 91%. Combining all, the ensemble system achieved
an accuracy of around 92.6%.
| 2,018 | Computation and Language |
Hierarchical Generative Modeling for Controllable Speech Synthesis | This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model
which can control latent attributes in the generated speech that are rarely
annotated in the training data, such as speaking style, accent, background
noise, and recording conditions. The model is formulated as a conditional
generative model based on the variational autoencoder (VAE) framework, with two
levels of hierarchical latent variables. The first level is a categorical
variable, which represents attribute groups (e.g. clean/noisy) and provides
interpretability. The second level, conditioned on the first, is a multivariate
Gaussian variable, which characterizes specific attribute configurations (e.g.
noise level, speaking rate) and enables disentangled fine-grained control over
these attributes. This amounts to using a Gaussian mixture model (GMM) for the
latent distribution. Extensive evaluation demonstrates its ability to control
the aforementioned attributes. In particular, we train a high-quality
controllable TTS model on real found data, which is capable of inferring
speaker and style attributes from a noisy utterance and use it to synthesize
clean speech with controllable speaking style.
| 2,018 | Computation and Language |
Exploring Sentence Vector Spaces through Automatic Summarization | Given vector representations for individual words, it is necessary to compute
vector representations of sentences for many applications in a compositional
manner, often using artificial neural networks.
Relatively little work has explored the internal structure and properties of
such sentence vectors. In this paper, we explore the properties of sentence
vectors in the context of automatic summarization. In particular, we show that
cosine similarity between sentence vectors and document vectors is strongly
correlated with sentence importance and that vector semantics can identify and
correct gaps between the sentences chosen so far and the document. In addition,
we identify specific dimensions which are linked to effective summaries. To our
knowledge, this is the first time specific dimensions of sentence embeddings
have been connected to sentence properties. We also compare the features of
different methods of sentence embeddings. Many of these insights have
applications in uses of sentence embeddings far beyond summarization.
| 2,018 | Computation and Language |
A Span-Extraction Dataset for Chinese Machine Reading Comprehension | Machine Reading Comprehension (MRC) has become enormously popular recently
and has attracted a lot of attention. However, the existing reading
comprehension datasets are mostly in English. In this paper, we introduce a
Span-Extraction dataset for Chinese machine reading comprehension to add
language diversities in this area. The dataset is composed by near 20,000 real
questions annotated on Wikipedia paragraphs by human experts. We also annotated
a challenge set which contains the questions that need comprehensive
understanding and multi-sentence inference throughout the context. We present
several baseline systems as well as anonymous submissions for demonstrating the
difficulties in this dataset. With the release of the dataset, we hosted the
Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC
2018). We hope the release of the dataset could further accelerate the Chinese
machine reading comprehension research. Resources are available:
https://github.com/ymcui/cmrc2018
| 2,019 | Computation and Language |
Analysis of Railway Accidents' Narratives Using Deep Learning | Automatic understanding of domain specific texts in order to extract useful
relationships for later use is a non-trivial task. One such relationship would
be between railroad accidents' causes and their correspondent descriptions in
reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B.
Railroads involved in accidents are required to submit an accident report to
the Federal Railroad Administration (FRA). These reports contain a variety of
fixed field entries including primary cause of the accidents (a coded variable
with 389 values) as well as a narrative field which is a short text description
of the accident. Although these narratives provide more information than a
fixed field entry, the terminologies used in these reports are not easy to
understand by a non-expert reader. Therefore, providing an assisting method to
fill in the primary cause from such domain specific texts(narratives) would
help to label the accidents with more accuracy. Another important question for
transportation safety is whether the reported accident cause is consistent with
narrative description. To address these questions, we applied deep learning
methods together with powerful word embeddings such as Word2Vec and GloVe to
classify accident cause values for the primary cause field using the text in
the narratives. The results show that such approaches can both accurately
classify accident causes based on report narratives and find important
inconsistencies in accident reporting.
| 2,020 | Computation and Language |
Sequence to Sequence Mixture Model for Diverse Machine Translation | Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated
translations. This can be attributed to the limitation of SEQ2SEQ models in
capturing lexical and syntactic variations in a parallel corpus resulting from
different styles, genres, topics, or ambiguity of the translation process. In
this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that
improves both translation diversity and quality by adopting a committee of
specialized translation models rather than a single translation model. Each
mixture component selects its own training dataset via optimization of the
marginal loglikelihood, which leads to a soft clustering of the parallel
corpus. Experiments on four language pairs demonstrate the superiority of our
mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted
beam search. Our mixture model uses negligible additional parameters and incurs
no extra computation cost during decoding.
| 2,018 | Computation and Language |
Exploring Textual and Speech information in Dialogue Act Classification
with Speaker Domain Adaptation | In spite of the recent success of Dialogue Act (DA) classification, the
majority of prior works focus on text-based classification with oracle
transcriptions, i.e. human transcriptions, instead of Automatic Speech
Recognition (ASR)'s transcriptions. In spoken dialog systems, however, the
agent would only have access to noisy ASR transcriptions, which may further
suffer performance degradation due to domain shift. In this paper, we explore
the effectiveness of using both acoustic and textual signals, either oracle or
ASR transcriptions, and investigate speaker domain adaptation for DA
classification. Our multimodal model proves to be superior to the unimodal
models, particularly when the oracle transcriptions are not available. We also
propose an effective method for speaker domain adaptation, which achieves
competitive results.
| 2,018 | Computation and Language |
Multi-Task Deep Learning for Legal Document Translation, Summarization
and Multi-Label Classification | The digitalization of the legal domain has been ongoing for a couple of
years. In that process, the application of different machine learning (ML)
techniques is crucial. Tasks such as the classification of legal documents or
contract clauses as well as the translation of those are highly relevant. On
the other side, digitized documents are barely accessible in this field,
particularly in Germany. Today, deep learning (DL) is one of the hot topics
with many publications and various applications. Sometimes it provides results
outperforming the human level. Hence this technique may be feasible for the
legal domain as well. However, DL requires thousands of samples to provide
decent results. A potential solution to this problem is multi-task DL to enable
transfer learning. This approach may be able to overcome the data scarcity
problem in the legal domain, specifically for the German language. We applied
the state of the art multi-task model on three tasks: translation,
summarization, and multi-label classification. The experiments were conducted
on legal document corpora utilizing several task combinations as well as
various model parameters. The goal was to find the optimal configuration for
the tasks at hand within the legal domain. The multi-task DL approach
outperformed the state of the art results in all three tasks. This opens a new
direction to integrate DL technology more efficiently in the legal domain.
| 2,018 | Computation and Language |
An Analysis of Attention Mechanisms: The Case of Word Sense
Disambiguation in Neural Machine Translation | Recent work has shown that the encoder-decoder attention mechanisms in neural
machine translation (NMT) are different from the word alignment in statistical
machine translation. In this paper, we focus on analyzing encoder-decoder
attention mechanisms, in the case of word sense disambiguation (WSD) in NMT
models. We hypothesize that attention mechanisms pay more attention to context
tokens when translating ambiguous words. We explore the attention distribution
patterns when translating ambiguous nouns. Counter-intuitively, we find that
attention mechanisms are likely to distribute more attention to the ambiguous
noun itself rather than context tokens, in comparison to other nouns. We
conclude that attention mechanism is not the main mechanism used by NMT models
to incorporate contextual information for WSD. The experimental results suggest
that NMT models learn to encode contextual information necessary for WSD in the
encoder hidden states. For the attention mechanism in Transformer models, we
reveal that the first few layers gradually learn to "align" source and target
tokens and the last few layers learn to extract features from the related but
unaligned context tokens.
| 2,018 | Computation and Language |
Super Characters: A Conversion from Sentiment Classification to Image
Classification | We propose a method named Super Characters for sentiment classification. This
method converts the sentiment classification problem into image classification
problem by projecting texts into images and then applying CNN models for
classification. Text features are extracted automatically from the generated
Super Characters images, hence there is no need of any explicit step of
embedding the words or characters into numerical vector representations.
Experimental results on large social media corpus show that the Super
Characters method consistently outperforms other methods for sentiment
classification and topic classification tasks on ten large social media
datasets of millions of contents in four different languages, including
Chinese, Japanese, Korean and English.
| 2,018 | Computation and Language |
Unsupervised Neural Text Simplification | The paper presents a first attempt towards unsupervised neural text
simplification that relies only on unlabeled text corpora. The core framework
is composed of a shared encoder and a pair of attentional-decoders and gains
knowledge of simplification through discrimination based-losses and denoising.
The framework is trained using unlabeled text collected from en-Wikipedia dump.
Our analysis (both quantitative and qualitative involving human evaluators) on
a public test data shows that the proposed model can perform
text-simplification at both lexical and syntactic levels, competitive to
existing supervised methods. Addition of a few labelled pairs also improves the
performance further.
| 2,019 | Computation and Language |
Semantic Parsing for Task Oriented Dialog using Hierarchical
Representations | Task oriented dialog systems typically first parse user utterances to
semantic frames comprised of intents and slots. Previous work on task oriented
intent and slot-filling work has been restricted to one intent per query and
one slot label per token, and thus cannot model complex compositional requests.
Alternative semantic parsing systems have represented queries as logical forms,
but these are challenging to annotate and parse. We propose a hierarchical
annotation scheme for semantic parsing that allows the representation of
compositional queries, and can be efficiently and accurately parsed by standard
constituency parsing models. We release a dataset of 44k annotated queries
(fb.me/semanticparsingdialog), and show that parsing models outperform
sequence-to-sequence approaches on this dataset.
| 2,018 | Computation and Language |
A Temporally Sensitive Submodularity Framework for Timeline
Summarization | Timeline summarization (TLS) creates an overview of long-running events via
dated daily summaries for the most important dates. TLS differs from standard
multi-document summarization (MDS) in the importance of date selection,
interdependencies between summaries of different dates and by having very short
summaries compared to the number of corpus documents. However, we show that MDS
optimization models using submodular functions can be adapted to yield
well-performing TLS models by designing objective functions and constraints
that model the temporal dimension inherent in TLS. Importantly, these
adaptations retain the elegance and advantages of the original MDS models
(clear separation of features and inference, performance guarantees and
scalability, little need for supervision) that current TLS-specific models
lack. An open-source implementation of the framework and all models described
in this paper is available online.
| 2,018 | Computation and Language |
Discourse Embellishment Using a Deep Encoder-Decoder Network | We suggest a new NLG task in the context of the discourse generation pipeline
of computational storytelling systems. This task, textual embellishment, is
defined by taking a text as input and generating a semantically equivalent
output with increased lexical and syntactic complexity. Ideally, this would
allow the authors of computational storytellers to implement just lightweight
NLG systems and use a domain-independent embellishment module to translate its
output into more literary text. We present promising first results on this task
using LSTM Encoder-Decoder networks trained on the WikiLarge dataset.
Furthermore, we introduce "Compiled Computer Tales", a corpus of
computationally generated stories, that can be used to test the capabilities of
embellishment algorithms.
| 2,018 | Computation and Language |
Adversarial TableQA: Attention Supervision for Question Answering on
Tables | The task of answering a question given a text passage has shown great
developments on model performance thanks to community efforts in building
useful datasets. Recently, there have been doubts whether such rapid progress
has been based on truly understanding language. The same question has not been
asked in the table question answering (TableQA) task, where we are tasked to
answer a query given a table. We show that existing efforts, of using "answers"
for both evaluation and supervision for TableQA, show deteriorating
performances in adversarial settings of perturbations that do not affect the
answer. This insight naturally motivates to develop new models that understand
question and table more precisely. For this goal, we propose Neural Operator
(NeOp), a multi-layer sequential network with attention supervision to answer
the query given a table. NeOp uses multiple Selective Recurrent Units (SelRUs)
to further help the interpretability of the answers of the model. Experiments
show that the use of operand information to train the model significantly
improves the performance and interpretability of TableQA models. NeOp
outperforms all the previous models by a big margin.
| 2,018 | Computation and Language |
Contextual Topic Modeling For Dialog Systems | Accurate prediction of conversation topics can be a valuable signal for
creating coherent and engaging dialog systems. In this work, we focus on
context-aware topic classification methods for identifying topics in free-form
human-chatbot dialogs. We extend previous work on neural topic classification
and unsupervised topic keyword detection by incorporating conversational
context and dialog act features. On annotated data, we show that incorporating
context and dialog acts leads to relative gains in topic classification
accuracy by 35% and on unsupervised keyword detection recall by 11% for
conversational interactions where topics frequently span multiple utterances.
We show that topical metrics such as topical depth is highly correlated with
dialog evaluation metrics such as coherence and engagement implying that
conversational topic models can predict user satisfaction. Our work for
detecting conversation topics and keywords can be used to guide chatbots
towards coherent dialog.
| 2,018 | Computation and Language |
Large-scale Hierarchical Alignment for Data-driven Text Rewriting | We propose a simple unsupervised method for extracting pseudo-parallel
monolingual sentence pairs from comparable corpora representative of two
different text styles, such as news articles and scientific papers. Our
approach does not require a seed parallel corpus, but instead relies solely on
hierarchical search over pre-trained embeddings of documents and sentences. We
demonstrate the effectiveness of our method through automatic and extrinsic
evaluation on text simplification from the normal to the Simple Wikipedia. We
show that pseudo-parallel sentences extracted with our method not only
supplement existing parallel data, but can even lead to competitive performance
on their own.
| 2,019 | Computation and Language |
Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric
and Circulant Weight Matrices | Currently, the biaffine classifier has been attracting attention as a method
to introduce an attention mechanism into the modeling of binary relations. For
instance, in the field of dependency parsing, the Deep Biaffine Parser by Dozat
and Manning has achieved state-of-the-art performance as a graph-based
dependency parser on the English Penn Treebank and CoNLL 2017 shared task. On
the other hand, it is reported that parameter redundancy in the weight matrix
in biaffine classifiers, which has O(n^2) parameters, results in overfitting (n
is the number of dimensions). In this paper, we attempted to reduce the
parameter redundancy by assuming either symmetry or circularity of weight
matrices. In our experiments on the CoNLL 2017 shared task dataset, our model
achieved better or comparable accuracy on most of the treebanks with more than
16% parameter reduction.
| 2,018 | Computation and Language |
Impact of Corpora Quality on Neural Machine Translation | Large parallel corpora that are automatically obtained from the web,
documents or elsewhere often exhibit many corrupted parts that are bound to
negatively affect the quality of the systems and models that learn from these
corpora. This paper describes frequent problems found in data and such data
affects neural machine translation systems, as well as how to identify and deal
with them. The solutions are summarised in a set of scripts that remove
problematic sentences from input corpora.
| 2,018 | Computation and Language |
STACL: Simultaneous Translation with Implicit Anticipation and
Controllable Latency using Prefix-to-Prefix Framework | Simultaneous translation, which translates sentences before they are
finished, is useful in many scenarios but is notoriously difficult due to
word-order differences. While the conventional seq-to-seq framework is only
suitable for full-sentence translation, we propose a novel prefix-to-prefix
framework for simultaneous translation that implicitly learns to anticipate in
a single translation model. Within this framework, we present a very simple yet
surprisingly effective wait-k policy trained to generate the target sentence
concurrently with the source sentence, but always k words behind. Experiments
show our strategy achieves low latency and reasonable quality (compared to
full-sentence translation) on 4 directions: zh<->en and de<->en.
| 2,019 | Computation and Language |
Efficient Dependency-Guided Named Entity Recognition | Named entity recognition (NER), which focuses on the extraction of
semantically meaningful named entities and their semantic classes from text,
serves as an indispensable component for several down-stream natural language
processing (NLP) tasks such as relation extraction and event extraction.
Dependency trees, on the other hand, also convey crucial semantic-level
information. It has been shown previously that such information can be used to
improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012).
In this work, we investigate on how to better utilize the structured
information conveyed by dependency trees to improve the performance of NER.
Specifically, unlike existing approaches which only exploit dependency
information for designing local features, we show that certain global
structured information of the dependency trees can be exploited when building
NER models where such information can provide guided learning and inference.
Through extensive experiments, we show that our proposed novel
dependency-guided NER model performs competitively with models based on
conventional semi-Markov conditional random fields, while requiring
significantly less running time.
| 2,018 | Computation and Language |
Weak Semi-Markov CRFs for NP Chunking in Informal Text | This paper introduces a new annotated corpus based on an existing informal
text corpus: the NUS SMS Corpus (Chen and Kan, 2013). The new corpus includes
76,490 noun phrases from 26,500 SMS messages, annotated by university students.
We then explored several graphical models, including a novel variant of the
semi-Markov conditional random fields (semi-CRF) for the task of noun phrase
chunking. We demonstrated through empirical evaluations on the new dataset that
the new variant yielded similar accuracy but ran in significantly lower running
time compared to the conventional semi-CRF.
| 2,016 | Computation and Language |
Learning to Recognize Discontiguous Entities | This paper focuses on the study of recognizing discontiguous entities.
Motivated by a previous work, we propose to use a novel hypergraph
representation to jointly encode discontiguous entities of unbounded length,
which can overlap with one another. To compare with existing approaches, we
first formally introduce the notion of model ambiguity, which defines the
difficulty level of interpreting the outputs of a model, and then formally
analyze the theoretical advantages of our model over previous existing
approaches based on linear-chain CRFs. Our empirical results also show that our
model is able to achieve significantly better results when evaluated on
standard data with many discontiguous entities.
| 2,016 | Computation and Language |
Mainumby: un Ayudante para la Traducci\'on Castellano-Guaran\'i | A wide range of applications play an important role in the daily work of the
modern human translator. However, the computational tools designed to aid in
the process of translation only benefit translation from or to a small minority
of the 7,000 languages of the world, those that we may call "privileged
languages". As for those translators who work with the remaining languages, the
marginalized languages in the digital world, they cannot benefit from the tools
that are speeding up the production of translation in the privileged languages.
We may ask whether it is possible to bridge the gap between what is available
for these languages and for the marginalized ones. This paper proposes a
framework for computer-assisted translation into marginalized languages and its
implementation in a web application for Spanish-Guarani translation. The
proposed system is based on a new theory for phrase-level translation in
contexts where adequate bilingual corpora are not available: Translation by
Generalized Segments (referred to as Minimal Dependency Translation in previous
work).
| 2,018 | Computation and Language |
An Exploration of Dropout with RNNs for Natural Language Inference | Dropout is a crucial regularization technique for the Recurrent Neural
Network (RNN) models of Natural Language Inference (NLI). However, dropout has
not been evaluated for the effectiveness at different layers and dropout rates
in NLI models. In this paper, we propose a novel RNN model for NLI and
empirically evaluate the effect of applying dropout at different layers in the
model. We also investigate the impact of varying dropout rates at these layers.
Our empirical evaluation on a large (Stanford Natural Language Inference
(SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward
connection severely affects the model accuracy at increasing dropout rates. We
also show that regularizing the embedding layer is efficient for SNLI whereas
regularizing the recurrent layer improves the accuracy for SciTail. Our model
achieved an accuracy 86.14% on the SNLI dataset and 77.05% on SciTail.
| 2,018 | Computation and Language |
Optimizing Segmentation Granularity for Neural Machine Translation | In neural machine translation (NMT), it is has become standard to translate
using subword units to allow for an open vocabulary and improve accuracy on
infrequent words. Byte-pair encoding (BPE) and its variants are the predominant
approach to generating these subwords, as they are unsupervised, resource-free,
and empirically effective. However, the granularity of these subword units is a
hyperparameter to be tuned for each language and task, using methods such as
grid search. Tuning may be done inexhaustively or skipped entirely due to
resource constraints, leading to sub-optimal performance. In this paper, we
propose a method to automatically tune this parameter using only one training
pass. We incrementally introduce new vocabulary online based on the held-out
validation loss, beginning with smaller, general subwords and adding larger,
more specific units over the course of training. Our method matches the results
found with grid search, optimizing segmentation granularity without any
additional training time. We also show benefits in training efficiency and
performance improvements for rare words due to the way embeddings for larger
units are incrementally constructed by combining those from smaller units.
| 2,018 | Computation and Language |
A neural network to classify metaphorical violence on cable news | I present here an experimental system for identifying and annotating metaphor
in corpora. It is designed to plug in to Metacorps, an experimental web app for
annotating metaphor. As Metacorps users annotate metaphors, the system will use
user annotations as training data. When the system is confident, it will
suggest an identification and an annotation. Once approved by the user, this
becomes more training data. This naturally allows for transfer learning, where
the system can, with some known degree of reliability, classify one class of
metaphor after only being trained on another class of metaphor. For example, in
our metaphorical violence project, metaphors may be classified by the network
they were observed on, the grammatical subject or object of the violence
metaphor, or the violent word used (hit, attack, beat, etc.).
| 2,018 | Computation and Language |
Lightweight Convolutional Approaches to Reading Comprehension on SQuAD | Current state-of-the-art reading comprehension models rely heavily on
recurrent neural networks. We explored an entirely different approach to
question answering: a convolutional model. By their nature, these convolutional
models are fast to train and capture local dependencies well, though they can
struggle with longer-range dependencies and thus require augmentation to
achieve comparable performance to RNN-based models. We conducted over two dozen
controlled experiments with convolutional models and various
kernel/attention/regularization schemes to determine the precise performance
gains of each strategy, while maintaining a focus on speed. We ultimately
ensembled three models: crossconv (0.5398 dev F1), attnconv (0.5665), and
maybeconv (0.5285). The ensembled model was able to achieve a 0.6238 F1 score
using the official SQuAD evaluation script. Our individual convolutional model
crossconv was able to exceed the performance of the RNN-plus-attention baseline
by 25% while training 6 times faster.
| 2,018 | Computation and Language |
pioNER: Datasets and Baselines for Armenian Named Entity Recognition | In this work, we tackle the problem of Armenian named entity recognition,
providing silver- and gold-standard datasets as well as establishing baseline
results on popular models. We present a 163000-token named entity corpus
automatically generated and annotated from Wikipedia, and another 53400-token
corpus of news sentences with manual annotation of people, organization and
location named entities. The corpora were used to train and evaluate several
popular named entity recognition models. Alongside the datasets, we release
50-, 100-, 200-, 300-dimensional GloVe word embeddings trained on a collection
of Armenian texts from Wikipedia, news, blogs, and encyclopedia.
| 2,020 | Computation and Language |
Learning Personas from Dialogue with Attentive Memory Networks | The ability to infer persona from dialogue can have applications in areas
ranging from computational narrative analysis to personalized dialogue
generation. We introduce neural models to learn persona embeddings in a
supervised character trope classification task. The models encode dialogue
snippets from IMDB into representations that can capture the various categories
of film characters. The best-performing models use a multi-level attention
mechanism over a set of utterances. We also utilize prior knowledge in the form
of textual descriptions of the different tropes. We apply the learned
embeddings to find similar characters across different movies, and cluster
movies according to the distribution of the embeddings. The use of short
conversational text as input, and the ability to learn from prior knowledge
using memory, suggests these methods could be applied to other domains.
| 2,018 | Computation and Language |
Named Entity Recognition on Twitter for Turkish using Semi-supervised
Learning with Word Embeddings | Recently, due to the increasing popularity of social media, the necessity for
extracting information from informal text types, such as microblog texts, has
gained significant attention. In this study, we focused on the Named Entity
Recognition (NER) problem on informal text types for Turkish. We utilized a
semi-supervised learning approach based on neural networks. We applied a fast
unsupervised method for learning continuous representations of words in vector
space. We made use of these obtained word embeddings, together with language
independent features that are engineered to work better on informal text types,
for generating a Turkish NER system on microblog texts. We evaluated our
Turkish NER system on Twitter messages and achieved better F-score performances
than the published results of previously proposed NER systems on Turkish
tweets. Since we did not employ any language dependent features, we believe
that our method can be easily adapted to microblog texts in other
morphologically rich languages.
| 2,018 | Computation and Language |
Improving Multilingual Semantic Textual Similarity with Shared Sentence
Encoder for Low-resource Languages | Measuring the semantic similarity between two sentences (or Semantic Textual
Similarity - STS) is fundamental in many NLP applications. Despite the
remarkable results in supervised settings with adequate labeling, little
attention has been paid to this task in low-resource languages with
insufficient labeling. Existing approaches mostly leverage machine translation
techniques to translate sentences into rich-resource language. These approaches
either beget language biases, or be impractical in industrial applications
where spoken language scenario is more often and rigorous efficiency is
required. In this work, we propose a multilingual framework to tackle the STS
task in a low-resource language e.g. Spanish, Arabic , Indonesian and Thai, by
utilizing the rich annotation data in a rich resource language, e.g. English.
Our approach is extended from a basic monolingual STS framework to a shared
multilingual encoder pretrained with translation task to incorporate
rich-resource language data. By exploiting the nature of a shared multilingual
encoder, one sentence can have multiple representations for different target
translation language, which are used in an ensemble model to improve similarity
evaluation. We demonstrate the superiority of our method over other state of
the art approaches on SemEval STS task by its significant improvement on non-MT
method, as well as an online industrial product where MT method fails to beat
baseline while our approach still has consistently improvements.
| 2,018 | Computation and Language |
Collective Learning From Diverse Datasets for Entity Typing in the Wild | Entity typing (ET) is the problem of assigning labels to given entity
mentions in a sentence. Existing works for ET require knowledge about the
domain and target label set for a given test instance. ET in the absence of
such knowledge is a novel problem that we address as ET in the wild. We
hypothesize that the solution to this problem is to build supervised models
that generalize better on the ET task as a whole, rather than a specific
dataset. In this direction, we propose a Collective Learning Framework (CLF),
which enables learning from diverse datasets in a unified way. The CLF first
creates a unified hierarchical label set (UHLS) and a label mapping by
aggregating label information from all available datasets. Then it builds a
single neural network classifier using UHLS, label mapping, and a partial loss
function. The single classifier predicts the finest possible label across all
available domains even though these labels may not be present in any
domain-specific dataset. We also propose a set of evaluation schemes and
metrics to evaluate the performance of models in this novel problem. Extensive
experimentation on seven diverse real-world datasets demonstrates the efficacy
of our CLF.
| 2,021 | Computation and Language |
Hierarchical Text Generation using an Outline | Many challenges in natural language processing require generating text,
including language translation, dialogue generation, and speech recognition.
For all of these problems, text generation becomes more difficult as the text
becomes longer. Current language models often struggle to keep track of
coherence for long pieces of text. Here, we attempt to have the model construct
and use an outline of the text it generates to keep it focused. We find that
the usage of an outline improves perplexity. We do not find that using the
outline improves human evaluation over a simpler baseline, revealing a
discrepancy in perplexity and human perception. Similarly, hierarchical
generation is not found to improve human evaluation scores.
| 2,018 | Computation and Language |
Modeling Composite Labels for Neural Morphological Tagging | Neural morphological tagging has been regarded as an extension to POS tagging
task, treating each morphological tag as a monolithic label and ignoring its
internal structure. We propose to view morphological tags as composite labels
and explicitly model their internal structure in a neural sequence tagger. For
this, we explore three different neural architectures and compare their
performance with both CRF and simple neural multiclass baselines. We evaluate
our models on 49 languages and show that the neural architecture that models
the morphological labels as sequences of morphological category values performs
significantly better than both baselines establishing state-of-the-art results
in morphological tagging for most languages.
| 2,018 | Computation and Language |
Abstractive Summarization Using Attentive Neural Techniques | In a world of proliferating data, the ability to rapidly summarize text is
growing in importance. Automatic summarization of text can be thought of as a
sequence to sequence problem. Another area of natural language processing that
solves a sequence to sequence problem is machine translation, which is rapidly
evolving due to the development of attention-based encoder-decoder networks.
This work applies these modern techniques to abstractive summarization. We
perform analysis on various attention mechanisms for summarization with the
goal of developing an approach and architecture aimed at improving the state of
the art. In particular, we modify and optimize a translation model with
self-attention for generating abstractive sentence summaries. The effectiveness
of this base model along with attention variants is compared and analyzed in
the context of standardized evaluation sets and test metrics. However, we show
that these metrics are limited in their ability to effectively score
abstractive summaries, and propose a new approach based on the intuition that
an abstractive model requires an abstractive evaluation.
| 2,018 | Computation and Language |
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence
Inference | Reasoning about implied relationships (e.g., paraphrastic, common sense,
encyclopedic) between pairs of words is crucial for many cross-sentence
inference problems. This paper proposes new methods for learning and using
embeddings of word pairs that implicitly represent background knowledge about
such relationships. Our pairwise embeddings are computed as a compositional
function on word representations, which is learned by maximizing the pointwise
mutual information (PMI) with the contexts in which the two words co-occur. We
add these representations to the cross-sentence attention layer of existing
inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or
replacing existing word embeddings. Experiments show a gain of 2.7% on the
recently released SQuAD2.0 and 1.3% on MultiNLI. Our representations also aid
in better generalization with gains of around 6-7% on adversarial SQuAD
datasets, and 8.8% on the adversarial entailment test set by Glockner et al.
(2018).
| 2,019 | Computation and Language |
BCWS: Bilingual Contextual Word Similarity | This paper introduces the first dataset for evaluating English-Chinese
Bilingual Contextual Word Similarity, namely BCWS
(https://github.com/MiuLab/BCWS). The dataset consists of 2,091 English-Chinese
word pairs with the corresponding sentential contexts and their similarity
scores annotated by the human. Our annotated dataset has higher consistency
compared to other similar datasets. We establish several baselines for the
bilingual embedding task to benchmark the experiments. Modeling cross-lingual
sense representations as provided in this dataset has the potential of moving
artificial intelligence from monolingual understanding towards multilingual
understanding.
| 2,018 | Computation and Language |
Constituent Parsing as Sequence Labeling | We introduce a method to reduce constituent parsing to sequence labeling. For
each word w_t, it generates a label that encodes: (1) the number of ancestors
in the tree that the words w_t and w_{t+1} have in common, and (2) the
nonterminal symbol at the lowest common ancestor. We first prove that the
proposed encoding function is injective for any tree without unary branches. In
practice, the approach is made extensible to all constituency trees by
collapsing unary branches. We then use the PTB and CTB treebanks as testbeds
and propose a set of fast baselines. We achieve 90.7% F-score on the PTB test
set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In
addition, sacrificing some accuracy, our approach achieves the fastest
constituent parsing speeds reported to date on PTB by a wide margin.
| 2,019 | Computation and Language |
Transition-based Parsing with Lighter Feed-Forward Networks | We explore whether it is possible to build lighter parsers, that are
statistically equivalent to their corresponding standard version, for a wide
set of languages showing different structures and morphologies. As testbed, we
use the Universal Dependencies and transition-based dependency parsers trained
on feed-forward networks. For these, most existing research assumes de facto
standard embedded features and relies on pre-computation tricks to obtain
speed-ups. We explore how these features and their size can be reduced and
whether this translates into speed-ups with a negligible impact on accuracy.
The experiments show that grand-daughter features can be removed for the
majority of treebanks without a significant (negative or positive) LAS
difference. They also show how the size of the embeddings can be notably
reduced.
| 2,018 | Computation and Language |
Labeling Gaps Between Words: Recognizing Overlapping Mentions with
Mention Separators | In this paper, we propose a new model that is capable of recognizing
overlapping mentions. We introduce a novel notion of mention separators that
can be effectively used to capture how mentions overlap with one another. On
top of a novel multigraph representation that we introduce, we show that
efficient and exact inference can still be performed. We present some
theoretical analysis on the differences between our model and a recently
proposed model for recognizing overlapping mentions, and discuss the possible
implications of the differences. Through extensive empirical analysis on
standard datasets, we demonstrate the effectiveness of our approach.
| 2,018 | Computation and Language |
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act
Classification | Recognising dialogue acts (DA) is important for many natural language
processing tasks such as dialogue generation and intention recognition. In this
paper, we propose a dual-attention hierarchical recurrent neural network for DA
classification. Our model is partially inspired by the observation that
conversational utterances are normally associated with both a DA and a topic,
where the former captures the social act and the latter describes the subject
matter. However, such a dependency between DAs and topics has not been utilised
by most existing systems for DA classification. With a novel dual task-specific
attention mechanism, our model is able, for utterances, to capture information
about both DAs and topics, as well as information about the interactions
between them. Experimental results show that by modelling topic as an auxiliary
task, our model can significantly improve DA classification, yielding better or
comparable performance to the state-of-the-art method on three public datasets.
| 2,019 | Computation and Language |
Named Entity Disambiguation using Deep Learning on Graphs | We tackle \ac{NED} by comparing entities in short sentences with \wikidata{}
graphs. Creating a context vector from graphs through deep learning is a
challenging problem that has never been applied to \ac{NED}. Our main
contribution is to present an experimental study of recent neural techniques,
as well as a discussion about which graph features are most important for the
disambiguation task. In addition, a new dataset (\wikidatadisamb{}) is created
to allow a clean and scalable evaluation of \ac{NED} with \wikidata{} entries,
and to be used as a reference in future research. In the end our results show
that a \ac{Bi-LSTM} encoding of the graph triplets performs best, improving
upon the baseline models and scoring an \rm{F1} value of $91.6\%$ on the
\wikidatadisamb{} test set
| 2,020 | Computation and Language |
BioSentVec: creating sentence embeddings for biomedical texts | Sentence embeddings have become an essential part of today's natural language
processing (NLP) systems, especially together advanced deep learning methods.
Although pre-trained sentence encoders are available in the general domain,
none exists for biomedical texts to date. In this work, we introduce
BioSentVec: the first open set of sentence embeddings trained with over 30
million documents from both scholarly articles in PubMed and clinical notes in
the MIMIC-III Clinical Database. We evaluate BioSentVec embeddings in two
sentence pair similarity tasks in different text genres. Our benchmarking
results demonstrate that the BioSentVec embeddings can better capture sentence
semantics compared to the other competitive alternatives and achieve
state-of-the-art performance in both tasks. We expect BioSentVec to facilitate
the research and development in biomedical text mining and to complement the
existing resources in biomedical word embeddings. BioSentVec is publicly
available at https://github.com/ncbi-nlp/BioSentVec
| 2,020 | Computation and Language |
WikiHow: A Large Scale Text Summarization Dataset | Sequence-to-sequence models have recently gained the state of the art
performance in summarization. However, not too many large-scale high-quality
datasets are available and almost all the available ones are mainly news
articles with specific writing style. Moreover, abstractive human-style systems
involving description of the content at a deeper level require data with higher
levels of abstraction. In this paper, we present WikiHow, a dataset of more
than 230,000 article and summary pairs extracted and constructed from an online
knowledge base written by different human authors. The articles span a wide
range of topics and therefore represent high diversity styles. We evaluate the
performance of the existing methods on WikiHow to present its challenges and
set some baselines to further improve it.
| 2,018 | Computation and Language |
Real-time Neural-based Input Method | The input method is an essential service on every mobile and desktop devices
that provides text suggestions. It converts sequential keyboard inputs to the
characters in its target language, which is indispensable for Japanese and
Chinese users. Due to critical resource constraints and limited network
bandwidth of the target devices, applying neural models to input method is not
well explored. In this work, we apply a LSTM-based language model to input
method and evaluate its performance for both prediction and conversion tasks
with Japanese BCCWJ corpus. We articulate the bottleneck to be the slow softmax
computation during conversion. To solve the issue, we propose incremental
softmax approximation approach, which computes softmax with a selected subset
vocabulary and fix the stale probabilities when the vocabulary is updated in
future steps. We refer to this method as incremental selective softmax. The
results show a two order speedup for the softmax computation when converting
Japanese input sequences with a large vocabulary, reaching real-time speed on
commodity CPU. We also exploit the model compressing potential to achieve a 92%
model size reduction without losing accuracy.
| 2,018 | Computation and Language |
Revisiting Distributional Correspondence Indexing: A Python
Reimplementation and New Experiments | This paper introduces PyDCI, a new implementation of Distributional
Correspondence Indexing (DCI) written in Python. DCI is a transfer learning
method for cross-domain and cross-lingual text classification for which we had
provided an implementation (here called JaDCI) built on top of JaTeCS, a Java
framework for text classification. PyDCI is a stand-alone version of DCI that
exploits scikit-learn and the SciPy stack. We here report on new experiments
that we have carried out in order to test PyDCI, and in which we use as
baselines new high-performing methods that have appeared after DCI was
originally proposed. These experiments show that, thanks to a few subtle ways
in which we have improved DCI, PyDCI outperforms both JaDCI and the
above-mentioned high-performing methods, and delivers the best known results on
the two popular benchmarks on which we had tested DCI, i.e.,
MultiDomainSentiment (a.k.a. MDS -- for cross-domain adaptation) and
Webis-CLS-10 (for cross-lingual adaptation). PyDCI, together with the code
allowing to replicate our experiments, is available at
https://github.com/AlexMoreo/pydci .
| 2,018 | Computation and Language |
Analyzing and Interpreting Convolutional Neural Networks in NLP | Convolutional neural networks have been successfully applied to various NLP
tasks. However, it is not obvious whether they model different linguistic
patterns such as negation, intensification, and clause compositionality to help
the decision-making process. In this paper, we apply visualization techniques
to observe how the model can capture different linguistic features and how
these features can affect the performance of the model. Later on, we try to
identify the model errors and their sources. We believe that interpreting CNNs
is the first step to understand the underlying semantic features which can
raise awareness to further improve the performance and explainability of CNN
models.
| 2,018 | Computation and Language |
Predictive Linguistic Features of Schizophrenia | Schizophrenia is one of the most disabling and difficult to treat of all
human medical/health conditions, ranking in the top ten causes of disability
worldwide. It has been a puzzle in part due to difficulty in identifying its
basic, fundamental components. Several studies have shown that some
manifestations of schizophrenia (e.g., the negative symptoms that include
blunting of speech prosody, as well as the disorganization symptoms that lead
to disordered language) can be understood from the perspective of linguistics.
However, schizophrenia research has not kept pace with technologies in
computational linguistics, especially in semantics and pragmatics. As such, we
examine the writings of schizophrenia patients analyzing their syntax,
semantics and pragmatics. In addition, we analyze tweets of (self pro-claimed)
schizophrenia patients who publicly discuss their diagnoses. For writing
samples dataset, syntactic features are found to be the most successful in
classification whereas for the less structured Twitter dataset, a combination
of features performed the best.
| 2,017 | Computation and Language |
Linguistic Legal Concept Extraction in Portuguese | This work investigates legal concepts and their expression in Portuguese,
concentrating on the "Order of Attorneys of Brazil" Bar exam. Using a corpus
formed by a collection of multiple-choice questions, three norms related to the
Ethics part of the OAB exam, language resources (Princeton WordNet and
OpenWordNet-PT) and tools (AntConc and Freeling), we began to investigate the
concepts and words missing from our repertory of concepts and words in
Portuguese, the knowledge base OpenWordNet-PT. We add these concepts and words
to OpenWordNet-PT and hence obtain a representation of these texts that is
"contained" in the lexical knowledge base.
| 2,018 | Computation and Language |
Proactive Security: Embedded AI Solution for Violent and Abusive Speech
Recognition | Violence is an epidemic in Brazil and a problem on the rise world-wide.
Mobile devices provide communication technologies which can be used to monitor
and alert about violent situations. However, current solutions, like panic
buttons or safe words, might increase the loss of life in violent situations.
We propose an embedded artificial intelligence solution, using natural language
and speech processing technology, to silently alert someone who can help in
this situation. The corpus used contains 400 positive phrases and 800 negative
phrases, totaling 1,200 sentences which are classified using two well-known
extraction methods for natural language processing tasks: bag-of-words and word
embeddings and classified with a support vector machine. We describe the
proof-of-concept product in development with promising results, indicating a
path towards a commercial product. More importantly we show that model
improvements via word embeddings and data augmentation techniques provide an
intrinsically robust model. The final embedded solution also has a small
footprint of less than 10 MB.
| 2,018 | Computation and Language |
Automatically Detecting Self-Reported Birth Defect Outcomes on Twitter
for Large-scale Epidemiological Research | In recent work, we identified and studied a small cohort of Twitter users
whose pregnancies with birth defect outcomes could be observed via their
publicly available tweets. Exploiting social media's large-scale potential to
complement the limited methods for studying birth defects, the leading cause of
infant mortality, depends on the further development of automatic methods. The
primary objective of this study was to take the first step towards scaling the
use of social media for observing pregnancies with birth defect outcomes,
namely, developing methods for automatically detecting tweets by users
reporting their birth defect outcomes. We annotated and pre-processed
approximately 23,000 tweets that mention birth defects in order to train and
evaluate supervised machine learning algorithms, including feature-engineered
and deep learning-based classifiers. We also experimented with various
under-sampling and over-sampling approaches to address the class imbalance. A
Support Vector Machine (SVM) classifier trained on the original, imbalanced
data set, with n-grams, word clusters, and structural features, achieved the
best baseline performance for the positive classes: an F1-score of 0.65 for the
"defect" class and 0.51 for the "possible defect" class. Our contributions
include (i) natural language processing (NLP) and supervised machine learning
methods for automatically detecting tweets by users reporting their birth
defect outcomes, (ii) a comparison of feature-engineered and deep
learning-based classifiers trained on imbalanced, under-sampled, and
over-sampled data, and (iii) an error analysis that could inform classification
improvements using our publicly available corpus. Future work will focus on
automating user-level analyses for cohort inclusion.
| 2,019 | Computation and Language |
Ordered Neurons: Integrating Tree Structures into Recurrent Neural
Networks | Natural language is hierarchically structured: smaller units (e.g., phrases)
are nested within larger units (e.g., clauses). When a larger constituent ends,
all of the smaller constituents that are nested within it must also be closed.
While the standard LSTM architecture allows different neurons to track
information at different time scales, it does not have an explicit bias towards
modeling a hierarchy of constituents. This paper proposes to add such an
inductive bias by ordering the neurons; a vector of master input and forget
gates ensures that when a given neuron is updated, all the neurons that follow
it in the ordering are also updated. Our novel recurrent architecture, ordered
neurons LSTM (ON-LSTM), achieves good performance on four different tasks:
language modeling, unsupervised parsing, targeted syntactic evaluation, and
logical inference.
| 2,019 | Computation and Language |
A Fully Attention-Based Information Retriever | Recurrent neural networks are now the state-of-the-art in natural language
processing because they can build rich contextual representations and process
texts of arbitrary length. However, recent developments on attention mechanisms
have equipped feedforward networks with similar capabilities, hence enabling
faster computations due to the increase in the number of operations that can be
parallelized. We explore this new type of architecture in the domain of
question-answering and propose a novel approach that we call Fully Attention
Based Information Retriever (FABIR). We show that FABIR achieves competitive
results in the Stanford Question Answering Dataset (SQuAD) while having fewer
parameters and being faster at both learning and inference than rival methods.
| 2,018 | Computation and Language |
Towards Universal Dialogue State Tracking | Dialogue state tracking is the core part of a spoken dialogue system. It
estimates the beliefs of possible user's goals at every dialogue turn. However,
for most current approaches, it's difficult to scale to large dialogue domains.
They have one or more of following limitations: (a) Some models don't work in
the situation where slot values in ontology changes dynamically; (b) The number
of model parameters is proportional to the number of slots; (c) Some models
extract features based on hand-crafted lexicons. To tackle these challenges, we
propose StateNet, a universal dialogue state tracker. It is independent of the
number of values, shares parameters across all slots, and uses pre-trained word
vectors instead of explicit semantic dictionaries. Our experiments on two
datasets show that our approach not only overcomes the limitations, but also
significantly outperforms the performance of state-of-the-art approaches.
| 2,018 | Computation and Language |
Biomedical Document Clustering and Visualization based on the Concepts
of Diseases | Document clustering is a text mining technique used to provide better
document search and browsing in digital libraries or online corpora. A lot of
research has been done on biomedical document clustering that is based on using
existing ontology. But, associations and co-occurrences of the medical concepts
are not well represented by using ontology. In this research, a vector
representation of concepts of diseases and similarity measurement between
concepts are proposed. They identify the closest concepts of diseases in the
context of a corpus. Each document is represented by using the vector space
model. A weight scheme is proposed to consider both local content and
associations between concepts. A Self-Organizing Map is used as document
clustering algorithm. The vector projection and visualization features of SOM
enable visualization and analysis of the clusters distributions and
relationships on the two dimensional space. The experimental results show that
the proposed document clustering framework generates meaningful clusters and
facilitate visualization of the clusters based on the concepts of diseases.
| 2,018 | Computation and Language |
Neural Transition-based Syntactic Linearization | The task of linearization is to find a grammatical order given a set of
words. Traditional models use statistical methods. Syntactic linearization
systems, which generate a sentence along with its syntactic tree, have shown
state-of-the-art performance. Recent work shows that a multi-layer LSTM
language model outperforms competitive statistical syntactic linearization
systems without using syntax. In this paper, we study neural syntactic
linearization, building a transition-based syntactic linearizer leveraging a
feed-forward neural network, observing significantly better results compared to
LSTM language models on this task.
| 2,018 | Computation and Language |
Semi-supervised acoustic model training for speech with code-switching | In the FAME! project, we aim to develop an automatic speech recognition (ASR)
system for Frisian-Dutch code-switching (CS) speech extracted from the archives
of a local broadcaster with the ultimate goal of building a spoken document
retrieval system. Unlike Dutch, Frisian is a low-resourced language with a very
limited amount of manually annotated speech data. In this paper, we describe
several automatic annotation approaches to enable using of a large amount of
raw bilingual broadcast data for acoustic model training in a semi-supervised
setting. Previously, it has been shown that the best-performing ASR system is
obtained by two-stage multilingual deep neural network (DNN) training using 11
hours of manually annotated CS speech (reference) data together with speech
data from other high-resourced languages. We compare the quality of
transcriptions provided by this bilingual ASR system with several other
approaches that use a language recognition system for assigning language labels
to raw speech segments at the front-end and using monolingual ASR resources for
transcription. We further investigate automatic annotation of the speakers
appearing in the raw broadcast data by first labeling with (pseudo) speaker
tags using a speaker diarization system and then linking to the known speakers
appearing in the reference data using a speaker recognition system. These
speaker labels are essential for speaker-adaptive training in the proposed
setting. We train acoustic models using the manually and automatically
annotated data and run recognition experiments on the development and test data
of the FAME! speech corpus to quantify the quality of the automatic
annotations. The ASR and CS detection results demonstrate the potential of
using automatic language and speaker tagging in semi-supervised bilingual
acoustic model training.
| 2,018 | Computation and Language |
Testing the Generalization Power of Neural Network Models Across NLI
Benchmarks | Neural network models have been very successful in natural language
inference, with the best models reaching 90% accuracy in some benchmarks.
However, the success of these models turns out to be largely benchmark
specific. We show that models trained on a natural language inference dataset
drawn from one benchmark fail to perform well in others, even if the notion of
inference assumed in these benchmarks is the same or similar. We train six high
performing neural network models on different datasets and show that each one
of these has problems of generalizing when we replace the original test set
with a test set taken from another corpus designed for the same task. In light
of these results, we argue that most of the current neural network models are
not able to generalize well in the task of natural language inference. We find
that using large pre-trained language models helps with transfer learning when
the datasets are similar enough. Our results also highlight that the current
NLI datasets do not cover the different nuances of inference extensively
enough.
| 2,019 | Computation and Language |
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference
Resolution | We introduce PreCo, a large-scale English dataset for coreference resolution.
The dataset is designed to embody the core challenges in coreference, such as
entity representation, by alleviating the challenge of low overlap between
training and test sets and enabling separated analysis of mention detection and
mention clustering. To strengthen the training-test overlap, we collect a large
corpus of about 38K documents and 12.4M words which are mostly from the
vocabulary of English-speaking preschoolers. Experiments show that with higher
training-test overlap, error analysis on PreCo is more efficient than the one
on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton
mentions making it possible for the first time to quantify the influence that a
mention detector makes on coreference resolution performance. The dataset is
freely available at https://preschool-lab.github.io/PreCo/.
| 2,018 | Computation and Language |
Object-oriented lexical encoding of multiword expressions: Short and
sweet | Multiword expressions (MWEs) exhibit both regular and idiosyncratic
properties. Their idiosyncrasy requires lexical encoding in parallel with their
component words. Their (at times intricate) regularity, on the other hand,
calls for means of flexible factorization to avoid redundant descriptions of
shared properties. However, so far, non-redundant general-purpose lexical
encoding of MWEs has not received a satisfactory solution. We offer a proof of
concept that this challenge might be effectively addressed within eXtensible
MetaGrammar (XMG), an object-oriented metagrammar framework. We first make an
existing metagrammatical resource, the FrenchTAG grammar, MWE-aware. We then
evaluate the factorization gain during incremental implementation with XMG on a
dataset extracted from an MWE-annotated reference corpus.
| 2,018 | Computation and Language |
Meta-Learning Multi-task Communication | In this paper, we describe a general framework: Parameters Read-Write
Networks (PRaWNs) to systematically analyze current neural models for
multi-task learning, in which we find that existing models expect to
disentangle features into different spaces while features learned in practice
are still entangled in shared space, leaving potential hazards for other
training or unseen tasks.
We propose to alleviate this problem by incorporating an inductive bias into
the process of multi-task learning, that each task can keep informed of not
only the knowledge stored in other tasks but the way how other tasks maintain
their knowledge.
In practice, we achieve above inductive bias by allowing different tasks to
communicate by passing both hidden variables and gradients explicitly.
Experimentally, we evaluate proposed methods on three groups of tasks and two
types of settings (\textsc{in-task} and \textsc{out-of-task}). Quantitative and
qualitative results show their effectiveness.
| 2,018 | Computation and Language |
Deep Graph Convolutional Encoders for Structured Data to Text Generation | Most previous work on neural text generation from graph-structured data
relies on standard sequence-to-sequence methods. These approaches linearise the
input graph to be fed to a recurrent neural network. In this paper, we propose
an alternative encoder based on graph convolutional networks that directly
exploits the input structure. We report results on two graph-to-sequence
datasets that empirically show the benefits of explicitly encoding the input
graph structure.
| 2,018 | Computation and Language |
Language Modeling at Scale | We show how Zipf's Law can be used to scale up language modeling (LM) to take
advantage of more training data and more GPUs. LM plays a key role in many
important natural language applications such as speech recognition and machine
translation. Scaling up LM is important since it is widely accepted by the
community that there is no data like more data. Eventually, we would like to
train on terabytes (TBs) of text (trillions of words). Modern training methods
are far from this goal, because of various bottlenecks, especially memory
(within GPUs) and communication (across GPUs). This paper shows how Zipf's Law
can address these bottlenecks by grouping parameters for common words and
character sequences, because $U \ll N$, where $U$ is the number of unique words
(types) and $N$ is the size of the training set (tokens). For a local batch
size $K$ with $G$ GPUs and a $D$-dimension embedding matrix, we reduce the
original per-GPU memory and communication asymptotic complexity from
$\Theta(GKD)$ to $\Theta(GK + UD)$. Empirically, we find $U \propto
(GK)^{0.64}$ on four publicly available large datasets. When we scale up the
number of GPUs to 64, a factor of 8, training time speeds up by factors up to
6.7$\times$ (for character LMs) and 6.3$\times$ (for word LMs) with negligible
loss of accuracy. Our weak scaling on 192 GPUs on the Tieba dataset shows a
35\% improvement in LM prediction accuracy by training on 93 GB of data
(2.5$\times$ larger than publicly available SOTA dataset), but taking only
1.25$\times$ increase in training time, compared to 3 GB of the same dataset
running on 6 GPUs.
| 2,018 | Computation and Language |
Exploiting Deep Representations for Neural Machine Translation | Advanced neural machine translation (NMT) models generally implement encoder
and decoder as multiple layers, which allows systems to model complex functions
and capture complicated linguistic structures. However, only the top layers of
encoder and decoder are leveraged in the subsequent process, which misses the
opportunity to exploit the useful information embedded in other layers. In this
work, we propose to simultaneously expose all of these signals with layer
aggregation and multi-layer attention mechanisms. In addition, we introduce an
auxiliary regularization term to encourage different layers to capture diverse
information. Experimental results on widely-used WMT14 English-German and WMT17
Chinese-English translation data demonstrate the effectiveness and universality
of the proposed approach.
| 2,018 | Computation and Language |
Modeling Localness for Self-Attention Networks | Self-attention networks have proven to be of profound value for its strength
of capturing global dependencies. In this work, we propose to model localness
for self-attention networks, which enhances the ability of capturing useful
local context. We cast localness modeling as a learnable Gaussian bias, which
indicates the central and scope of the local region to be paid more attention.
The bias is then incorporated into the original attention distribution to form
a revised distribution. To maintain the strength of capturing long distance
dependencies and enhance the ability of capturing short-range dependencies, we
only apply localness modeling to lower layers of self-attention networks.
Quantitative and qualitative analyses on Chinese-English and English-German
translation tasks demonstrate the effectiveness and universality of the
proposed approach.
| 2,018 | Computation and Language |
Multi-Head Attention with Disagreement Regularization | Multi-head attention is appealing for the ability to jointly attend to
information from different representation subspaces at different positions. In
this work, we introduce a disagreement regularization to explicitly encourage
the diversity among multiple attention heads. Specifically, we propose three
types of disagreement regularization, which respectively encourage the
subspace, the attended positions, and the output representation associated with
each attention head to be different from other heads. Experimental results on
widely-used WMT14 English-German and WMT17 Chinese-English translation tasks
demonstrate the effectiveness and universality of the proposed approach.
| 2,018 | Computation and Language |
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