pwc-archive/papers-with-abstracts huggingface dataset:
Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation
Authors: Akiko Eriguchi, Kazuma Hashimoto, Yoshimasa Tsuruoka
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Decoder, Machine Translation, NMT, Translation

This paper reports our systems (UT-AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT{'}16) and their results in the English-to-Japanese translation task. Our model is based on the tree-to-sequence Attention-based NMT (ANMT) model proposed by Eriguchi et al. (2016). We submitted two ANMT systems: one with a word-based decoder and the other with a character-based decoder. Experimenting on the English-to-Japanese translation task, we have confirmed that the character-based decoder can cover almost the full vocabulary in the target language and generate translations much faster than the word-based model.
Faster and Lighter Phrase-based Machine Translation Baseline
Authors: Liling Tan
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, NMT, Translation

This paper describes the SENSE machine translation system participation in the Third Workshop for Asian Translation (WAT2016). We share our best practices to build a fast and light phrase-based machine translation (PBMT) models that have comparable results to the baseline systems provided by the organizers. As Neural Machine Translation (NMT) overtakes PBMT as the state-of-the-art, deep learning and new MT practitioners might not be familiar with the PBMT paradigm and we hope that this paper will help them build a PBMT baseline system quickly and easily.
Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for Chinese--Japanese
Authors: Wei Yang, Yves Lepage
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Chinese Word Segmentation, Machine Translation, Segmentation, Term Extraction, Translation

Unlike European languages, many Asian languages like Chinese and Japanese do not have typographic boundaries in written system. Word segmentation (tokenization) that break sentences down into individual words (tokens) is normally treated as the first step for machine translation (MT). For Chinese and Japanese, different rules and segmentation tools lead different segmentation results in different level of granularity between Chinese and Japanese. To improve the translation accuracy, we adjust and balance the granularity of segmentation results around terms for Chinese{--}Japanese patent corpus for training translation model. In this paper, we describe a statistical machine translation (SMT) system which is built on re-tokenized Chinese-Japanese patent training corpus using extracted bilingual multi-word terms.
Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation
Authors: Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, Mamoru Komachi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Decoder, Machine Translation, Sentence, Text Summarization, Translation

In machine translation, we must consider the difference in expression between languages. For example, the active/passive voice may change in Japanese-English translation. The same verb in Japanese may be translated into different voices at each translation because the voice of a generated sentence cannot be determined using only the information of the Japanese sentence. Machine translation systems should consider the information structure to improve the coherence of the output by using several topicalization techniques such as passivization. Therefore, this paper reports on our attempt to control the voice of the sentence generated by an encoder-decoder model. To control the voice of the generated sentence, we added the voice information of the target sentence to the source sentence during the training. We then generated sentences with a specified voice by appending the voice information to the source sentence. We observed experimentally whether the voice could be controlled. The results showed that, we could control the voice of the generated sentence with 85.0{\%} accuracy on average. In the evaluation of Japanese-English translation, we obtained a 0.73-point improvement in BLEU score by using gold voice labels.
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016
Authors: Katsuhito Sudoh, Masaaki Nagata
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Chinese Word Segmentation, Dependency Parsing, General Classification, Learning-To-Rank, Machine Translation, Part-Of-Speech Tagging, Translation

This paper presents our Chinese-to-Japanese patent machine translation system for WAT 2016 (Group ID: ntt) that uses syntactic pre-ordering over Chinese dependency structures. Chinese words are reordered by a learning-to-rank model based on pairwise classification to obtain word order close to Japanese. In this year{'}s system, two different machine translation methods are compared: traditional phrase-based statistical machine translation and recent sequence-to-sequence neural machine translation with an attention mechanism. Our pre-ordering showed a significant improvement over the phrase-based baseline, but, in contrast, it degraded the neural machine translation baseline.
IITP English-Hindi Machine Translation System at WAT 2016
Authors: Sukanta Sen, Debajyoty Banik, Asif Ekbal, Pushpak Bhattacharyya
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

In this paper we describe the system that we develop as part of our participation in WAT 2016. We develop a system based on hierarchical phrase-based SMT for English to Hindi language pair. We perform re-ordering and augment bilingual dictionary to improve the performance. As a baseline we use a phrase-based SMT model. The MT models are fine-tuned on the development set, and the best configurations are used to report the evaluation on the test set. Experiments show the BLEU of 13.71 on the benchmark test data. This is better compared to the official baseline BLEU score of 10.79.
Residual Stacking of RNNs for Neural Machine Translation
Authors: Raphael Shu, Akiva Miura
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Decoder, Machine Translation, NMT, Translation, Word Embeddings

To enhance Neural Machine Translation models, several obvious ways such as enlarging the hidden size of recurrent layers and stacking multiple layers of RNN can be considered. Surprisingly, we observe that using naively stacked RNNs in the decoder slows down the training and leads to degradation in performance. In this paper, We demonstrate that applying residual connections in the depth of stacked RNNs can help the optimization, which is referred to as residual stacking. In empirical evaluation, residual stacking of decoder RNNs gives superior results compared to other methods of enhancing the model with a fixed parameter budget. Our submitted systems in WAT2016 are based on a NMT model ensemble with residual stacking in the decoder. To further improve the performance, we also attempt various methods of system combination in our experiments.
Analyzing Impact, Trend, and Diffusion of Knowledge associated with Neoplasms Research
Authors: Min Song
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Boundary Detection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentence

Cancer (a.k.a neoplasms in a broader sense) is one of the leading causes of death worldwide and its incidence is expected to exacerbate. To respond to the critical need from the society, there have been rigorous attempts for the cancer research community to develop treatment for cancer. Accordingly, we observe a surge in the sheer volume of research products and outcomes in relation to neoplasms. In this talk, we introduce the notion of entitymetrics to provide a new lens for understanding the impact, trend, and diffusion of knowledge associated with neoplasms research. To this end, we collected over two million records from PubMed, the most popular search engine in the medical domain. Coupled with text mining techniques including named entity recognition, sentence boundary detection, string approximate matching, entitymetrics enables us to analyze knowledge diffusion, impact, and trend at various knowledge entity units, such as bio-entity, organization, and country. At the end of the talk, the future applications and possible directions of entitymetrics will be discussed.
Local-Global Vectors to Improve Unigram Terminology Extraction
Authors: Ehsan Amjadian, Diana Inkpen, Tahereh Paribakht, Farahnaz Faez
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Term Extraction, Word Embeddings

The present paper explores a novel method that integrates efficient distributed representations with terminology extraction. We show that the information from a small number of observed instances can be combined with local and global word embeddings to remarkably improve the term extraction results on unigram terms. To do so we pass the terms extracted by other tools to a filter made of the local-global embeddings and a classifier which in turn decides whether or not a term candidate is a term. The filter can also be used as a hub to merge different term extraction tools into a single higher-performing system. We compare filters that use the skip-gram architecture and filters that employ the CBOW architecture for the task at hand.
Recognition of non-domain phrases in automatically extracted lists of terms
Authors: Agnieszka Mykowiecka, Malgorzata Marciniak, Piotr Rychlik
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Diversity, Term Extraction

In the paper, we address the problem of recognition of non-domain phrases in terminology lists obtained with an automatic term extraction tool. We focus on identification of multi-word phrases that are general terms and discourse function expressions. We tested several methods based on domain corpora comparison and a method based on contexts of phrases identified in a large corpus of general language. We compared the results of the methods to manual annotation. The results show that the task is quite hard as the inter-annotator agreement is low. Several tested methods achieved similar overall results, although the phrase ordering varied between methods. The most successful method with the precision about 0.75 at the half of the tested list was the context based method using a modified contextual diversity coefficient.
Contextual term equivalent search using domain-driven disambiguation
Authors: Caroline Barri{\`e}re, Pierre Andr{\'e} M{\'e}nard, Daphn{\'e}e Azoulay
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Articles, Translation

This article presents a domain-driven algorithm for the task of term sense disambiguation (TSD). TSD aims at automatically choosing which term record from a term bank best represents the meaning of a term occurring in a particular context. In a translation environment, finding the contextually appropriate term record is necessary to access the proper equivalent to be used in the target language text. The term bank TERMIUM Plus, recently published as an open access repository, is chosen as a domain-rich resource for testing our TSD algorithm, using English and French as source and target languages. We devise an experiment using over 1300 English terms found in scientific articles, and show that our domain-driven TSD algorithm is able to bring the best term record, and therefore the best French equivalent, at the average rank of 1.69 compared to a baseline random rank of 3.51.
A Method of Augmenting Bilingual Terminology by Taking Advantage of the Conceptual Systematicity of Terminologies
Authors: Miki Iwai, Koichi Takeuchi, Kyo Kageura, Kazuya Ishibashi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Transfer Learning

In this paper, we propose a method of augmenting existing bilingual terminologies. Our method belongs to a {``}generate and validate{''} framework rather than extraction from corpora. Although many studies have proposed methods to find term translations or to augment terminology within a {``}generate and validate{''} framework, few has taken full advantage of the systematic nature of terminologies. A terminology of a domain represents the conceptual system of the domain fairly systematically, and we contend that making use of the systematicity fully will greatly contribute to the effective augmentation of terminologies. This paper proposes and evaluates a novel method to generate bilingual term candidates by using existing terminologies and delving into their systematicity. Experiments have shown that our method can generate much better term candidate pairs than the existing method and give improved performance for terminology augmentation.
Acquisition of semantic relations between terms: how far can we get with standard NLP tools?
Authors: Ina Roesiger, Julia Bettinger, Johannes Sch{\"a}fer, Michael Dorna, Ulrich Heid
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Coreference Resolution, Lemmatization, Morphological Analysis, Text Classification

The extraction of data exemplifying relations between terms can make use, at least to a large extent, of techniques that are similar to those used in standard hybrid term candidate extraction, namely basic corpus analysis tools (e.g. tagging, lemmatization, parsing), as well as morphological analysis of complex words (compounds and derived items). In this article, we discuss the use of such techniques for the extraction of raw material for a description of relations between terms, and we provide internal evaluation data for the devices developed. We claim that user-generated content is a rich source of term variation through paraphrasing and reformulation, and that these provide relational data at the same time as term variants. Germanic languages with their rich word formation morphology may be particularly good candidates for the approach advocated here.
Evaluation of distributional semantic models: a holistic approach
Authors: Gabriel Bernier-Colborne, Patrick Drouin
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We investigate how both model-related factors and application-related factors affect the accuracy of distributional semantic models (DSMs) in the context of specialized lexicography, and how these factors interact. This holistic approach to the evaluation of DSMs provides valuable guidelines for the use of these models and insight into the kind of semantic information they capture.
A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification
Authors: Behrang QasemiZadeh
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification

We propose and evaluate a method for identifying co-hyponym lexical units in a terminological resource. The principles of term recognition and distributional semantics are combined to extract terms from a similar category of concept. Given a set of candidate terms, random projections are employed to represent them as low-dimensional vectors. These vectors are derived automatically from the frequency of the co-occurrences of the candidate terms and words that appear within windows of text in their proximity (context-windows). In a $k$-nearest neighbours framework, these vectors are classified using a small set of manually annotated terms which exemplify concept categories. We then investigate the interplay between the size of the corpus that is used for collecting the co-occurrences and a number of factors that play roles in the performance of the proposed method: the configuration of context-windows for collecting co-occurrences, the selection of neighbourhood size ($k$), and the choice of similarity metric.
Pattern-based Word Sketches for the Extraction of Semantic Relations
Authors: Pilar Le{\'o}n-Ara{\'u}z, Antonio San Mart{\'\i}n, Pamela Faber
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Despite advances in computer technology, terminologists still tend to rely on manual work to extract all the semantic information that they need for the description of specialized concepts. In this paper we propose the creation of new word sketches in Sketch Engine for the extraction of semantic relations. Following a pattern-based approach, new sketch grammars are devel-oped in order to extract some of the most common semantic relations used in the field of ter-minology: generic-specific, part-whole, location, cause and function.
Constructing and Evaluating Controlled Bilingual Terminologies
Authors: Rei Miyata, Kyo Kageura
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

This paper presents the construction and evaluation of Japanese and English controlled bilingual terminologies that are particularly intended for controlled authoring and machine translation with special reference to the Japanese municipal domain. Our terminologies are constructed by extracting terms from municipal website texts, and the term variations are controlled by defining preferred and proscribed terms for both the source Japanese and the target English. To assess the coverage of the terms/concepts in the municipal domain and validate the quality of the control, we employ a quantitative extrapolation method that estimates the potential vocabulary size. Using Large-Number-of-Rare-Event (LNRE) modelling, we compare two parameters: (1) uncontrolled and controlled and (2) Japanese and English. The results show that our terminologies currently cover about 45{--}65{\%} of the terms and 50{--}65{\%} of the concepts in the municipal domain, and are well controlled. The detailed analysis of growth patterns of terminologies also provides insight into the extent to which we can enlarge the terminologies within the realistic range.
Providing and Analyzing NLP Terms for our Community
Authors: Gil Francopoulo, Joseph Mariani, Patrick Paroubek, Fr{\'e}d{\'e}ric Vernier
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Named Entity Recognition (NER), Optical Character Recognition (OCR)

By its own nature, the Natural Language Processing (NLP) community is a priori the best equipped to study the evolution of its own publications, but works in this direction are rare and only recently have we seen a few attempts at charting the field. In this paper, we use the algorithms, resources, standards, tools and common practices of the NLP field to build a list of terms characteristic of ongoing research, by mining a large corpus of scientific publications, aiming at the largest possible exhaustivity and covering the largest possible time span. Study of the evolution of this term list through time reveals interesting insights on the dynamics of field and the availability of the term database and of the corpus (for a large part) make possible many further comparative studies in addition to providing a test field for a new graphic interface designed to perform visual time analytics of large sized thesauri.
Evaluating a dictionary of human phenotype terms focusing on rare diseases
Authors: Simon Kocbek, Toyofumi Fujiwara, Jin-Dong Kim, Toshihisa Takagi, Tudor Groza
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Annotating medical text such as clinical notes with human phenotype descriptors is an important task that can, for example, assist in building patient profiles. To automatically annotate text one usually needs a dictionary of predefined terms. However, do to the variety of human expressiveness, current state-of-the art phenotype concept recognizers and automatic annotators struggle with specific domain issues and challenges. In this paper we present results of an-notating gold standard corpus with a dictionary containing lexical variants for the Human Phenotype Ontology terms. The main purpose of the dictionary is to improve the recall of phenotype concept recognition systems. We compare the method with four other approaches and present results.
A semi automatic annotation approach for ontological and terminological knowledge acquisition
Authors: Driss Sadoun
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We propose a semi-automatic method for the acquisition of specialised ontological and terminological knowledge. An ontology and a terminology are automatically built from domain experts{'} annotations. The ontology formalizes the common and shared conceptual vocabulary of those experts. Its associated terminology defines a glossary linking annotated terms to their semantic categories. These two resources evolve incrementally and are used for an automatic annotation of a new corpus at each iteration. The annotated corpus concerns the evaluation of French higher education and science institutions.
Understanding Medical free text: A Terminology driven approach
Authors: Santosh Sai Krishna, Manoj Hans
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Boundary Detection

With many hospitals digitalizing clinical records it has opened opportunities for researchers in NLP, Machine Learning to apply techniques for extracting meaning and make actionable insights. There has been previous attempts in mapping free text to medical nomenclature like UMLS, SNOMED. However, in this paper, we had analyzed diagnosis in clinical reports using ICD10 to achieve a lightweight, real-time predictions by introducing concepts like WordInfo, root word identification. We were able to achieve 68.3{\%} accuracy over clinical records collected from qualified clinicians. Our study would further help the healthcare institutes in organizing their clinical reports based on ICD10 mappings and derive numerous insights to achieve operational efficiency and better medical care.
Discriminating between Similar Languages and Arabic Dialect Identification: A Report on the Third DSL Shared Task
Authors: Shervin Malmasi, Marcos Zampieri, Nikola Ljube{\v{s}}i{\'c}, Preslav Nakov, Ahmed Ali, J{\"o}rg Tiedemann
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Dialect Identification, General Classification, Language Identification, regression

We present the results of the third edition of the Discriminating between Similar Languages (DSL) shared task, which was organized as part of the VarDial{'}2016 workshop at COLING{'}2016. The challenge offered two subtasks: subtask 1 focused on the identification of very similar languages and language varieties in newswire texts, whereas subtask 2 dealt with Arabic dialect identification in speech transcripts. A total of 37 teams registered to participate in the task, 24 teams submitted test results, and 20 teams also wrote system description papers. High-order character n-grams were the most successful feature, and the best classification approaches included traditional supervised learning methods such as SVM, logistic regression, and language models, while deep learning approaches did not perform very well.
Discriminating Similar Languages with Linear SVMs and Neural Networks
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Taraka Rama
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Deep Learning, Language Identification

This paper describes the systems we experimented with for participating in the discriminating between similar languages (DSL) shared task 2016. We submitted results of a single system based on support vector machines (SVM) with linear kernel and using character ngram features, which obtained the first rank at the closed training track for test set A. Besides the linear SVM, we also report additional experiments with a number of deep learning architectures. Despite our intuition that non-linear deep learning methods should be advantageous, linear models seems to fare better in this task, at least with the amount of data and the amount of effort we spent on tuning these models.