December 11-16 2016 Osaka Japan

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December 12, 2016 (continued) 10:50–12:00 Poster presentation I (Research paper) Global Pre-ordering for Improving Sublanguage Translation Masaru Fuji, Masao Utiyama, Eiichiro Sumita and Yuji Matsumoto Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation Shin Kanouchi, Katsuhito Sudoh and Mamoru Komachi 10:50–12:00 Poster presentation I (System description) IIT Bombay’s English-Indonesian submission at WAT: Integrating Neural Language Models with SMT Sandhya Singh, Anoop Kunchukuttan and Pushpak Bhattacharyya Domain Adaptation and Attention-Based Unknown Word Replacement in Chineseto-Japanese Neural Machine Translation Kazuma Hashimoto, Akiko Eriguchi and Yoshimasa Tsuruoka System Description of bjtu_nlp Neural Machine Translation System Shaotong Li, JinAn Xu, Yufeng Chen and Yujie Zhang Translation systems and experimental results of the EHR group for WAT2016 tasks Terumasa Ehara Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST- CMU at WAT2016 Graham Neubig NICT-2 Translation System for WAT2016: Applying Domain Adaptation to Phrasebased Statistical Machine Translation Kenji Imamura and Eiichiro Sumita Translation Using JAPIO Patent Corpora: JAPIO at WAT2016 Satoshi Kinoshita, Tadaaki Oshio, Tomoharu Mitsuhashi and Terumasa Ehara xii
December 12, 2016 (continued) 12:00–14:00 Lunch 14:00–14:45 Invited talk Google’s Neural Machine Translation System: Training and Serving a Very Large Neural MT Models Hideto Kazawa 14:45–15:45 Research paper II An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation Xiaolin Wang, Andrew Finch, Masao Utiyama and Eiichiro Sumita Similar Southeast Asian Languages: Corpus-Based Case Study on Thai-Laotian and Malay-Indonesian Chenchen Ding, Masao Utiyama and Eiichiro Sumita Integrating empty category detection into preordering Machine Translation Shunsuke Takeno, Masaaki Nagata and Kazuhide Yamamoto 15:45–16:00 System description II Kyoto University Participation to WAT 2016 Fabien Cromieres, Chenhui Chu, Toshiaki Nakazawa and Sadao Kurohashi 16:00–16:05 Commemorative photo xiii
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 9 and 10: Table of Contents Overview of the 3
Page 11: Conference Program December 12, 201
Page 15 and 16: Overview of the 3rd Workshop on Asi
Page 17 and 18: LangPair Train Dev DevTest Test JPC
Page 19 and 20: ASPEC JPC IITB BPPT pivot System ID
Page 21 and 22: 3.6 Tree-to-String Syntax-based SMT
Page 23 and 24: • Use of other resources in addit
Page 25 and 26: ASPEC JPC BPPT IITBC pivot Team ID
Page 27 and 28: ut the adequacy is worse. As for AS
Page 29 and 30: Figure 3: Official evaluation resul
Page 37 and 38: Figure 11: Official evaluation resu
Page 39 and 40: Figure 13: Official evaluation resu
Page 41 and 42: Annotator A Annotator B all weighte
Page 43 and 44: Kyoto-U 2 bjtu nlp UT-KAY 1 UT-KAY
Page 45 and 46: Online B IITP-MT EHR Online A ≫
Page 47 and 48: SYSTEM ID ID METHOD OTHER RESOURCES
Page 49 and 50: SYSTEM ID ID METHOD OTHER BLEU RIBE
Page 59 and 60: Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
Page 61 and 62: Translation of Patent Sentences wit
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Chinese sentences contain fewer tha
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Figure 3: NMT decoding with technic
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Table 1: Automatic evaluation resul
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Figure 6: Example of correct transl
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K. Papineni, S. Roukos, T. Ward, an
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Table 1: Examples of title, ingredi
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text. In this section, we explain t
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Table 3: Automatic evaluation BLEU/
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Table 5: Number of fluency errors i
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Kenneth Heafield. 2011. Kenlm: Fast
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under-studied. MorphInd, a morphana
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dimensions of 150 units in second h
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distribution as received in JPO ade
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Domain Adaptation and Attention-Bas
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Once s M , which represents the ent
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3.4.2 Ensemble of Attention Scores
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Type Count Ratio (A) Correct 76 30.
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Minh-Thang Luong, Hieu Pham, and Ch
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Pair 1 Japanese [[ A ][ B ][ C ]
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ID n-grams len freq m1 prevent, | 1
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prediction accuracy with respect to
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Reference: [ A To provide] [ B a to
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Philipp Koehn. 2004. Statistical si
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Therefore, we propose to use phrase
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Figure 2: An NRM considering phrase
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Mono Swap D right D left Acc. The r
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Method ja-en en-ja BLEU RIBES WER B
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Peng Li, Yang Liu, and Maosong Sun.
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Figure 1: The framework of NMT. Whe
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For long sentence, we discarded all
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As shown in the above tables and fi
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Translation systems and experimenta
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The number of TM training sentence
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where, f, p and e means a source, p
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former system can translate more th
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Lexicons and Minimum Risk Training
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2.2 Parameter Optimization If we de
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ML (λ=0.0) ML (λ=0.8) MR (λ=0.0)
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Graham Neubig. 2013. Travatar: A fo
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Feature Space Common (h ) Domain 1
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Preprocessing Training Translation
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JPC ASPEC LM Method Ja-En En-Ja Ja-
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Translation Using JAPIO Patent Corp
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4 Results Table 1 shows official ev
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Table 3: Examples of translation er
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An Efficient and Effective Online S
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#$ #%$ #$ !""
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This formula requires a context of
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Sentence Segmenter Parameters Dev.
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a meaningful baseline for related r
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Similar Southeast Asian Languages:
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τ around 0.71, and the Malay-Indon
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-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3
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1.800 1.600 1.400 1.200 1.000 0.800
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Integrating empty category detectio
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3 Preordering with empty categories
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We performed the tests under two co
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the decoded sentence and the refere
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Kenneth Heafield. 2011. Kenlm: Fast
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Figure 1: Overview of KyotoEBMT. Th
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proportionally slower to compute (a
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3.6 Ensembling Ensembling has previ
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when Chinese is also the language m
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Character-based Decoding in Tree-to
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where W s ∈ R d×d is a matrix an
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Model BLEU (BP) RIBES Character-bas
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The word-based NMT models cannot us
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Razvan Pascanu, Tomas Mikolov, and
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governs the grammaticality of the t
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2.1 Quantization and Binarization o
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3 Results Team Other Resources Syst
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Peter F Brown, John Cocke, Stephen
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Yonghui Wu, Mike Schuster, Zhifeng
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Language Sentence Chinese 该 / 钽
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Chinese or Japanese sentences Chine
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4 Experiments and Results 4.1 Chine
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6 Conclusion and Future Work In thi
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Controlling the Voice of a Sentence
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Figure 2: Flow of the voice predict
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ecause the number of passive senten
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controlling the sentence length wit
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Chinese-to-Japanese Patent Machine
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using the reordering oracles over t
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Conference on Natural Language Proc
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Figure 1: Hierarchical approach wit
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newswire test and development set o
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Source: the rain and cold wind on W
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Residual Stacking of RNNs for Neura
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2.2 Generalized Minimum Bayes Risk
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5.0 4.5 4.0 Baseline model Enlarged
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Felix Hill, Kyunghyun Cho, Sebastie
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December 11-16 2016 Osaka Japan

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