pwc-archive/papers-with-abstracts huggingface dataset:
XMU Neural Machine Translation Systems for WAT 2017
Authors: Boli Wang, Zhixing Tan, Jinming Hu, Yidong Chen, Xiaodong Shi
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Decoder, Machine Translation, Translation, Word Embeddings

This paper describes the Neural Machine Translation systems of Xiamen University for the shared translation tasks of WAT 2017. Our systems are based on the Encoder-Decoder framework with attention. We participated in three subtasks. We experimented subword segmentation, synthetic training data and model ensembling. Experiments show that all these methods can give substantial improvements.
A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size
Authors: Masato Neishi, Jin Sakuma, Satoshi Tohda, Shonosuke Ishiwatari, Naoki Yoshinaga, Masashi Toyoda
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Machine Translation, Translation, Word Embeddings

In this paper, we describe the team UT-IIS{'}s system and results for the WAT 2017 translation tasks. We further investigated several tricks including a novel technique for initializing embedding layers using only the parallel corpus, which increased the BLEU score by 1.28, found a practical large batch size of 256, and gained insights regarding hyperparameter settings. Ultimately, our system obtained a better result than the state-of-the-art system of WAT 2016. Our code is available on \url{https://github.com/nem6ishi/wat17}.
Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017
Authors: Zi Long, Ryuichiro Kimura, Takehito Utsuro, Tomoharu Mitsuhashi, Mikio Yamamoto
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy. The selected phrases are then replaced with tokens during training and post-translated by the phrase translation table of SMT. In this paper, we apply the method proposed by Long et al. (2017) to the WAT 2017 Japanese-Chinese and Japanese-English patent datasets. Evaluation on Japanese-to-Chinese, Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent sentence translation proved the effectiveness of phrases selected with branching entropy, where the NMT model of Long et al.(2017) achieves a substantial improvement over a baseline NMT model without the technique proposed by Long et al.(2017).
SMT reranked NMT
Authors: Terumasa Ehara
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Machine Translation, NMT, Reranking, Translation

System architecture, experimental settings and experimental results of the EHR team for the WAT2017 tasks are described. We participate in three tasks: JPCen-ja, JPCzh-ja and JPCko-ja. Although the basic architecture of our system is NMT, reranking technique is conducted using SMT results. One of the major drawback of NMT is under-translation and over-translation. On the other hand, SMT infrequently makes such translations. So, using reranking of n-best NMT outputs by the SMT output, discarding such translations can be expected. We can improve BLEU score from 46.03 to 47.08 by this technique in JPCzh-ja task.
Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017
Authors: Kenji Imamura, Eiichiro Sumita
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Machine Translation, Reranking, Small Data Image Classification, Translation

In this paper, we describe the NICT-2 neural machine translation system evaluated at WAT2017. This system uses multiple models as an ensemble and combines models with opposite decoding directions by reranking (called bi-directional reranking). In our experimental results on small data sets, the translation quality improved when the number of models was increased to 32 in total and did not saturate. In the experiments on large data sets, improvements of 1.59-3.32 BLEU points were achieved when six-model ensembles were combined by the bi-directional reranking.
A Simple and Strong Baseline: NAIST-NICT Neural Machine Translation System for WAT2017 English-Japanese Translation Task
Authors: Yusuke Oda, Katsuhito Sudoh, Satoshi Nakamura, Masao Utiyama, Eiichiro Sumita
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Decoder, Machine Translation, Translation

This paper describes the details about the NAIST-NICT machine translation system for WAT2017 English-Japanese Scientific Paper Translation Task. The system consists of a language-independent tokenizer and an attentional encoder-decoder style neural machine translation model. According to the official results, our system achieves higher translation accuracy than any systems submitted previous campaigns despite simple model architecture.
Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017
Authors: Satoshi Kinoshita, Tadaaki Oshio, Tomoharu Mitsuhashi
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Information Retrieval, Machine Translation, NMT, Sentence, Translation

Japio participates in patent subtasks (JPC-EJ/JE/CJ/KJ) with phrase-based statistical machine translation (SMT) and neural machine translation (NMT) systems which are trained with its own patent corpora in addition to the subtask corpora provided by organizers of WAT2017. In EJ and CJ subtasks, SMT and NMT systems whose sizes of training corpora are about 50 million and 10 million sentence pairs respectively achieved comparable scores for automatic evaluations, but NMT systems were superior to SMT systems for both official and in-house human evaluations.
Kyoto University Participation to WAT 2017
Authors: Fabien Cromieres, Raj Dabre, Toshiaki Nakazawa, Sadao Kurohashi
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

We describe here our approaches and results on the WAT 2017 shared translation tasks. Following our good results with Neural Machine Translation in the previous shared task, we continue this approach this year, with incremental improvements in models and training methods. We focused on the ASPEC dataset and could improve the state-of-the-art results for Chinese-to-Japanese and Japanese-to-Chinese translations.
CUNI NMT System for WAT 2017 Translation Tasks
Authors: Tom Kocmi, Du{\v{s}}an Vari{\v{s}}, Ond{\v{r}}ej Bojar
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Machine Translation, NMT, Sentence, Translation

The paper presents this year{'}s CUNI submissions to the WAT 2017 Translation Task focusing on the Japanese-English translation, namely Scientific papers subtask, Patents subtask and Newswire subtask. We compare two neural network architectures, the standard sequence-to-sequence with attention (Seq2Seq) and an architecture using convolutional sentence encoder (FBConv2Seq), both implemented in the NMT framework Neural Monkey that we currently participate in developing. We also compare various types of preprocessing of the source Japanese sentences and their impact on the overall results. Furthermore, we include the results of our experiments with out-of-domain data obtained by combining the corpora provided for each subtask.
Tokyo Metropolitan University Neural Machine Translation System for WAT 2017
Authors: Yukio Matsumura, Mamoru Komachi
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Machine Translation, NMT, Translation

In this paper, we describe our neural machine translation (NMT) system, which is based on the attention-based NMT and uses long short-term memories (LSTM) as RNN. We implemented beam search and ensemble decoding in the NMT system. The system was tested on the 4th Workshop on Asian Translation (WAT 2017) shared tasks. In our experiments, we participated in the scientific paper subtasks and attempted Japanese-English, English-Japanese, and Japanese-Chinese translation tasks. The experimental results showed that implementation of beam search and ensemble decoding can effectively improve the translation quality.
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation
Authors: S. Singh, hya, Ritesh Panjwani, Anoop Kunchukuttan, Pushpak Bhattacharyya
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Decoder, Image Captioning, Language Modeling, Language Modelling, Machine Translation, Question Answering, Translation, Word Embeddings

In this paper, we empirically compare the two encoder-decoder neural machine translation architectures: convolutional sequence to sequence model (ConvS2S) and recurrent sequence to sequence model (RNNS2S) for English-Hindi language pair as part of IIT Bombay{'}s submission to WAT2017 shared task. We report the results for both English-Hindi and Hindi-English direction of language pair.
Automatic detection of stance towards vaccination in online discussion forums
Authors: Maria Skeppstedt, Andreas Kerren, Manfred Stede
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Binary Classification, Sentiment Analysis, Stance Detection

A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance {`}against{'} or {`}for{'} vaccination, or as {`}undecided{'}. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance {`}against{'} vaccination from stance {`}for{'} vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Future work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features.
Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals
Authors: Noor Fazilla Abd Yusof, Chenghua Lin, Frank Guerin
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11

We develop a computational model to discover the potential causes of depression by analysing the topics in a usergenerated text. We show the most prominent causes, and how these causes evolve over time. Also, we highlight the differences in causes between students with low and high neuroticism. Our studies demonstrate that the topics reveal valuable clues about the causes contributing to depressed mood. Identifying causes can have a significant impact on improving the quality of depression care; thereby providing greater insights into a patient{'}s state for pertinent treatment recommendations. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment.
Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation
Authors: Ryo Takeuchi, Hayate Iso, Kaoru Ito, Shoko Wakamiya, Eiji Aramaki
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Event Detection, regression

To date, various Twitter-based event detection systems have been proposed. Most of their targets, however, share common characteristics. They are seasonal or global events such as earthquakes and flu pandemics. In contrast, this study targets unseasonal and local disease events. Our system investigates the frequencies of disease-related words such as {``}nausea{''},{``}chill{''},and {``}diarrhea{''} and estimates the number of patients using regression of these word frequencies. Experiments conducted using Japanese 47 areas from January 2017 to April 2017 revealed that the detection of small and unseasonal event is extremely difficult (overall performance: 0.13). However, we found that the event scale and the detection performance show high correlation in the specified cases (in the phase of patient increasing or decreasing). The results also suggest that when 150 and more patients appear in a high population area, we can expect that our social sensors detect this outbreak. Based on these results, we can infer that social sensors can reliably detect unseasonal and local disease events under certain conditions, just as they can for seasonal or global events.
Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
Authors: Yi-Jie Huang, Chu Hsien Su, Yi-Chun Chang, Tseng-Hsin Ting, Tzu-Yuan Fu, Rou-Min Wang, Hong-Jie Dai, Yung-Chun Chang, Jitendra Jonnagaddala, Wen-Lian Hsu
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: General Classification

The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper{'}s pregnancy stage. Another interesting application is to use the social media platforms to analyze users{'} health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets.
Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information
Authors: Chen-Kai Wang, Onkar Singh, Zhao-Li Tang, Hong-Jie Dai
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: General Classification, valid

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it{'}s often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to out-breaks in real time. In this paper we apply the long short-term memory recurrent neural net-work (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1{\%} of the highest score achieved by SVM with oversampling technique.
ZikaHack 2016: A digital disease detection competition
Authors: Dillon C Adam, Jitendra Jonnagaddala, Daniel Han-Chen, Sean Batongbacal, Luan Almeida, Jing Z Zhu, Jenny J Yang, Jumail M Mundekkat, Steven Badman, Abrar Chughtai, C Raina MacIntyre
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11

Effective response to infectious diseases outbreaks relies on the rapid and early detection of those outbreaks. Invalidated, yet timely and openly available digital information can be used for the early detection of outbreaks. Public health surveillance authorities can exploit these early warnings to plan and co-ordinate rapid surveillance and emergency response programs. In 2016, a digital disease detection competition named ZikaHack was launched. The objective of the competition was for multidisciplinary teams to design, develop and demonstrate innovative digital disease detection solutions to retrospectively detect the 2015-16 Brazilian Zika virus outbreak earlier than traditional surveillance methods. In this paper, an overview of the ZikaHack competition is provided. The challenges and lessons learned in organizing this competition are also discussed for use by other researchers interested in organizing similar competitions.
A Method to Generate a Machine-Labeled Data for Biomedical Named Entity Recognition with Various Sub-Domains
Authors: Juae Kim, Sunjae Kwon, Youngjoong Ko, Jungyun Seo
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: BIG-bench Machine Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering

Biomedical Named Entity (NE) recognition is a core technique for various works in the biomedical domain. In previous studies, using machine learning algorithm shows better performance than dictionary-based and rule-based approaches because there are too many terminological variations of biomedical NEs and new biomedical NEs are constantly generated. To achieve the high performance with a machine-learning algorithm, good-quality corpora are required. However, it is difficult to obtain the good-quality corpora because an-notating a biomedical corpus for ma-chine-learning is extremely time-consuming and costly. In addition, most previous corpora are insufficient for high-level tasks because they cannot cover various domains. Therefore, we propose a method for generating a large amount of machine-labeled data that covers various domains. To generate a large amount of machine-labeled data, firstly we generate an initial machine-labeled data by using a chunker and MetaMap. The chunker is developed to extract only biomedical NEs with manually annotated data. MetaMap is used to annotate the category of bio-medical NE. Then we apply the self-training approach to bootstrap the performance of initial machine-labeled data. In our experiments, the biomedical NE recognition system that is trained with our proposed machine-labeled data achieves much high performance. As a result, our system outperforms biomedical NE recognition system that using MetaMap only with 26.03{\%}p improvements on F1-score.
Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble
Authors: Jing Cyun Tu, Po-Ting Lai, Richard Tzong-Han Tsai
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Classification, Feature Engineering, General Classification, Named Entity Recognition (NER), Sentence

The study of drug-drug interaction (DDI) is important in the drug discovering. Both PubMed and DrugBank are rich resources to retrieve DDI information which is usually represented in plain text. Automatically extracting DDI pairs from text improves the quality of drug discov-ering. In this paper, we presented a study that focuses on the DDI classification. We normalized the drug names, and developed both sentence-level and corpus-level features for DDI classification. A classifier ensemble approach is used for the unbalance DDI labels problem. Our approach achieved an F-score of 65.4{\%} on SemEval 2013 DDI test set. The experimental results also show the effects of proposed corpus-level features in the DDI task.
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model
Authors: Neha Warikoo, Yung-Chun Chang, Wen-Lian Hsu
Date: 2017-11-01 00:00:00
Proceeding: WS 2017 11
Tasks: Feature Engineering, Relation, Relation Extraction

In this work, we introduce a novel feature engineering approach named {``}algebraic invariance{''} to identify discriminative patterns for learning relation pair features for the chemical-disease relation (CDR) task of BioCreative V. Our method exploits the existing structural similarity of the key concepts of relation descriptions from the CDR corpus to generate robust linguistic patterns for SVM tree kernel-based learning. Preprocessing of the training data classifies the entity pairs as either related or unrelated to build instance types for both inter-sentential and intra-sentential scenarios. An invariant function is proposed to process and optimally cluster similar patterns for both positive and negative instances. The learning model for CDR pairs is based on the SVM tree kernel approach, which generates feature trees and vectors and is modeled on suitable invariance based patterns, bringing brevity, precision and context to the identifier features. Results demonstrate that our method outperformed other compared approaches, achieved a high recall rate of 85.08{\%}, and averaged an F1-score of 54.34{\%} without the use of any additional knowledge bases.
NTUCLE: Developing a Corpus of Learner English to Provide Writing Support for Engineering Students
Authors: Roger Vivek Placidus Winder, Joseph MacKinnon, Shu Yun Li, Benedict Christopher Tzer Liang Lin, Carmel Lee Hah Heah, Lu{\'\i}s Morgado da Costa, Takayuki Kuribayashi, Francis Bond
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: TAG

This paper describes the creation of a new annotated learner corpus. The aim is to use this corpus to develop an automated system for corrective feedback on students{'} writing. With this system, students will be able to receive timely feedback on language errors before they submit their assignments for grading. A corpus of assignments submitted by first year engineering students was compiled, and a new error tag set for the NTU Corpus of Learner English (NTUCLE) was developed based on that of the NUS Corpus of Learner English (NUCLE), as well as marking rubrics used at NTU. After a description of the corpus, error tag set and annotation process, the paper presents the results of the annotation exercise as well as follow up actions. The final error tag set, which is significantly larger than that for the NUCLE error categories, is then presented before a brief conclusion summarising our experience and future plans.
Understanding Non-Native Writings: Can a Parser Help?
Authors: Jirka Hana, Barbora Hladk{\'a}
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12

We present a pilot study on parsing non-native texts written by learners of Czech. We performed experiments that have shown that at least high-level syntactic functions, like subject, predicate, and object, can be assigned based on a parser trained on standard native language.
Carrier Sentence Selection for Fill-in-the-blank Items
Authors: Shu Jiang, John Lee
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Position, Sentence

Fill-in-the-blank items are a common form of exercise in computer-assisted language learning systems. To automatically generate an effective item, the system must be able to select a high-quality carrier sentence that illustrates the usage of the target word. Previous approaches for carrier sentence selection have considered sentence length, vocabulary difficulty, the position of the target word and the presence of finite verbs. This paper investigates the utility of word co-occurrence statistics and lexical similarity as selection criteria. In an evaluation on generating fill-in-the-blank items for learning Chinese as a foreign language, we show that these two criteria can improve carrier sentence quality.