December 11-16 2016 Osaka Japan

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Table of Contents Overview of the 3rd Workshop on Asian Translation Toshiaki Nakazawa, Chenchen Ding, Hideya MINO, Isao Goto, Graham Neubig and Sadao Kurohashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation Zi Long, Takehito Utsuro, Tomoharu Mitsuhashi and Mikio Yamamoto . . . . . . . . . . . . . . . . . . . . . . 47 Japanese-English Machine Translation of Recipe Texts Takayuki Sato, Jun Harashima and Mamoru Komachi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 IIT Bombay’s English-Indonesian submission at WAT: Integrating Neural Language Models with SMT Sandhya Singh, Anoop Kunchukuttan and Pushpak Bhattacharyya . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation Kazuma Hashimoto, Akiko Eriguchi and Yoshimasa Tsuruoka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Global Pre-ordering for Improving Sublanguage Translation Masaru Fuji, Masao Utiyama, Eiichiro Sumita and Yuji Matsumoto . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation Shin Kanouchi, Katsuhito Sudoh and Mamoru Komachi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 System Description of bjtu_nlp Neural Machine Translation System Shaotong Li, JinAn Xu, Yufeng Chen and Yujie Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Translation systems and experimental results of the EHR group for WAT2016 tasks Terumasa Ehara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016 Graham Neubig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 NICT-2 Translation System for WAT2016: Applying Domain Adaptation to Phrase-based Statistical Machine Translation Kenji Imamura and Eiichiro Sumita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Translation Using JAPIO Patent Corpora: JAPIO at WAT2016 Satoshi Kinoshita, Tadaaki Oshio, Tomoharu Mitsuhashi and Terumasa Ehara . . . . . . . . . . . . . . . 133 An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation Xiaolin Wang, Andrew Finch, Masao Utiyama and Eiichiro Sumita . . . . . . . . . . . . . . . . . . . . . . . . . 139 Similar Southeast Asian Languages: Corpus-Based Case Study on Thai-Laotian and Malay-Indonesian Chenchen Ding, Masao Utiyama and Eiichiro Sumita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Integrating empty category detection into preordering Machine Translation Shunsuke Takeno, Masaaki Nagata and Kazuhide Yamamoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Kyoto University Participation to WAT 2016 Fabien Cromieres, Chenhui Chu, Toshiaki Nakazawa and Sadao Kurohashi. . . . . . . . . . . . . . . . . .166 ix
Page 1 and 2: WAT 2016 The 3rd Workshop on Asian
Page 3: Preface Many Asian countries are ra
Page 6 and 7: Technical Collaborators Luis Fernan
Page 10 and 11: Character-based Decoding in Tree-to
Page 12 and 13: December 12, 2016 (continued) 10:50
Page 14 and 15: December 12, 2016 (continued) 16:05
Page 16 and 17: LangPair Train Dev DevTest Test ASP
Page 18 and 19: LangPair Train Dev Test Monolingual
Page 20 and 21: 3.1 Training Data We used the follo
Page 22 and 23: 8 Figure 1: The submission web page
Page 24 and 25: 5 All important information is tran
Page 26 and 27: 7 Evaluation Results In this sectio
Page 28 and 29: Figure 2: Official evaluation resul
Page 36 and 37: Figure 10: Official evaluation resu
Page 42 and 43: NAIST 1 NAIST 2 Kyoto-U 1 Kyoto-U 2
Page 44 and 45: JAPIO 1 JAPIO 2 NICT-2 1 EHR (2015)
Page 46 and 47: Figure 15: The chronological evalua
Page 48 and 49: SYSTEM ID ID METHOD OTHER BLEU RIBE
Page 54 and 55: SYSTEM ID ID METHOD OTHER RESOURCES
Page 56 and 57: SYSTEM ID ID METHOD OTHER RESOURCES
Page 58 and 59:
References Rafael E. Banchs, Luis F
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Hayahide Yamagishi, Shin Kanouchi,
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Figure 1: Example of translation er
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Figure 2: NMT training after replac
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Figure 4: NMT rescoring of 1,000-be
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Figure 5: Example of correct transl
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y Bahdanau et al. (2015), which int
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Japanese-English Machine Translatio
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NMT embeds each source word into a
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Accuracy (General) It is applied wh
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(13) 4 1 3 4 from pan
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7 Conclusion and Future Work In thi
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IIT Bombay’s English-Indonesian s
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These 4 systems were trained for bo
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The scores clearly indicate that bo
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Durrani, N., Fraser, A. M., Schmid,
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(or topics), such as computer scien
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The model parameters are optimized
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Dev. data Test data Method BLEU RIB
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BIO CHEM ENE ENVI INFO MATE Method
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Global Pre-ordering for Improving S
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To provide an image device capable
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96.5 97.5 Accuracy [%] 96.0 95.5 95
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Table 1: Evaluation of Japanese-to-
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References Vamshi Ambati and Wei Ch
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Neural Reordering Model Considering
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Figure 1: Four orientations, namely
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Dimension Description 1 Word transl
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Figure 3: The accuracy of reorderin
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It worked fairly well for some lang
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System Description of bjtu_nlp Neur
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to encode the source sentence (Schu
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RIBES BLEU Task System BLEU RIBES H
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Srivastava, Nitish, et al. 2014. Dr
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JPCko-ja task, because of the word
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address the problem. (1) All parent
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For all tasks using the reordering,
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Trevor Cohn and Mirella Lapata. 200
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First, an encoder converts the sour
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This matrix can be precomputed duri
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WAT “HUMAN” metric (Nakazawa et
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NICT-2 Translation System for WAT20
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• For the common space, where all
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JPC Method Ja-En En-Ja Ja-Zh Zh-Ja
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Isao Goto, Masao Utiyama, Eiichiro
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2 Systems We used two SMT systems t
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of functional words and errors of p
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Masao Utiyama and Hiroshi Isahara.
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Figure 1: Illustration of Online Se
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Algorithm 1 Online Sentence Segment
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Corpus # Sent. Pairs Japanese Engli
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Input Oracle Segments Oracle Transl
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Stephan Peitz, Markus Freitag, Arne
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Thai Malay คุ ณ จะ ท
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Thai (th) Laotian (lo) Malay (ms) I
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1.800 1.600 1.400 1.200 1.000 0.800
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1.800 1.600 1.400 1.200 1.000 0.800
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ecause it is the subject of a matri
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source tree to decide whether its c
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Figure 3: Characteristic of machine
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Success translation Reference i ’
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Kyoto University Participation to W
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Figure 2: The structure of a NMT sy
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the noise, and is not directly link
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Subtask Ja → En En → Ja Ja →
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Seiya Tokui, Kenta Oono, Shohei Hid
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Figure 1: WAT’16 Our system: tree
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Sentences Parsed sentences Train da
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Time (msec / sentence) Word-based d
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Sepp Hochreiter and Jürgen Schmidh
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Faster and Lighter Phrase-based Mac
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At the onset of SMT, the importance
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EXT_BIN_DIR=/path/to/moses-training
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Team System BLEU HUMAN SENSE Vanill
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Philipp Koehn. 2004. Pharaoh: a bea
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Improving Patent Translation using
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3.1 Monolingual Multi-word Term Ext
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Components for multi-word terms in
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Filtering by thresholds P (a) Filte
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Hideki Mima and Sophia Ananiadou. 2
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Verb # Active # Passive # Total sho
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# Active # Passive # Error Accuracy
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Example 1 Source Reference This pa
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Toshiaki Nakazawa, Manabu Yaguchi,
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Chinese sentence Chinese word segme
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System JPO Adequacy Pairwise BLEU R
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IITP English-Hindi Machine Translat
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hierarchical phrases that contain s
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We evaluate each system using BLEU
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Michael Collins, Philipp Koehn, and
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In this paper, we explores the effe
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h t = f(c t , e t−1 , h t−1 ;
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Model RIBES(%) BLEU(%) HUMAN Online
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Author Index Banik, Debajyoty, 216
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December 11-16 2016 Osaka Japan

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