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References Rafael E. Banchs, Luis F. D’Haro, and Haizhou Li. 2015. Adequacy-fluency metrics: Evaluating mt in the continuous space model framework. IEEE/ACM Trans. Audio, Speech and Lang. Proc., 23(3):472–482, March. Jacob Cohen. 1968. Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4):213 – 220. Fabien Cromieres, Chenhui Chu, Toshiaki Nakazawa, and Sadao Kurohashi. 2016. Kyoto university participation to wat 2016. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 166–174, Osaka, Japan, December. The COLING 2016 Organizing Committee. Terumasa Ehara. 2016. Translation systems and experimental results of the ehr group for wat2016 tasks. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 111–118, Osaka, Japan, December. The COLING 2016 Organizing Committee. Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 2016. Character-based decoding in tree-tosequence attention-based neural machine translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 175–183, Osaka, Japan, December. The COLING 2016 Organizing Committee. J.L. Fleiss et al. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378–382. Kazuma Hashimoto, Akiko Eriguchi, and Yoshimasa Tsuruoka. 2016. Domain adaptation and attention-based unknown word replacement in chinese-to-japanese neural machine translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 75–83, Osaka, Japan, December. The COLING 2016 Organizing Committee. Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. 2013. Scalable modified kneser-ney language model estimation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 690–696, Sofia, Bulgaria, August. Association for Computational Linguistics. Hieu Hoang, Philipp Koehn, and Adam Lopez. 2009. A unified framework for phrase-based, hierarchical, and syntax-based statistical machine translation. In Proceedings of the International Workshop on Spoken Language Translation, pages 152–159. Kenji Imamura and Eiichiro Sumita. 2016. Nict-2 translation system for wat2016: Applying domain adaptation to phrase-based statistical machine translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 126–132, Osaka, Japan, December. The COLING 2016 Organizing Committee. Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010. Automatic evaluation of translation quality for distant language pairs. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP ’10, pages 944–952, Stroudsburg, PA, USA. Association for Computational Linguistics. Marcin Junczys-Dowmunt, Tomasz Dwojak, and Hieu Hoang. 2016. Is neural machine translation ready for deployment? a case study on 30 translation directions. Satoshi Kinoshita, Tadaaki Oshio, Tomoharu Mitsuhashi, and Terumasa Ehara. 2016. Translation using japio patent corpora: Japio at wat2016. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 133–138, Osaka, Japan, December. The COLING 2016 Organizing Committee. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: Open source toolkit for statistical machine translation. In Annual Meeting of the Association for Computational Linguistics (ACL), demonstration session. Philipp Koehn. 2004. Statistical significance tests for machine translation evaluation. In Dekang Lin and Dekai Wu, editors, Proceedings of EMNLP 2004, pages 388–395, Barcelona, Spain, July. Association for Computational Linguistics. T. Kudo. 2005. Mecab : Yet another part-of-speech and morphological analyzer. http://mecab.sourceforge.net/. Sadao Kurohashi, Toshihisa Nakamura, Yuji Matsumoto, and Makoto Nagao. 1994. Improvements of Japanese morphological analyzer JUMAN. In Proceedings of The International Workshop on Sharable Natural Language, pages 22–28. 44
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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 and 12: Conference Program December 12, 201
Page 13 and 14: December 12, 2016 (continued) 12:00
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 53 and 54: SYSTEM ID ID METHOD OTHER BLEU RIBE
Page 57: SYSTEM ID ID METHOD OTHER BLEU RIBE
Page 61 and 62: Translation of Patent Sentences wit
Page 63 and 64: Chinese sentences contain fewer tha
Page 65 and 66: Figure 3: NMT decoding with technic
Page 67 and 68: Table 1: Automatic evaluation resul
Page 69 and 70: Figure 6: Example of correct transl
Page 71 and 72: K. Papineni, S. Roukos, T. Ward, an
Page 73 and 74: Table 1: Examples of title, ingredi
Page 75 and 76: text. In this section, we explain t
Page 77 and 78: Table 3: Automatic evaluation BLEU/
Page 79 and 80: Table 5: Number of fluency errors i
Page 81 and 82: Kenneth Heafield. 2011. Kenlm: Fast
Page 83 and 84: under-studied. MorphInd, a morphana
Page 85 and 86: dimensions of 150 units in second h
Page 87 and 88: distribution as received in JPO ade
Page 89 and 90: Domain Adaptation and Attention-Bas
Page 91 and 92: Once s M , which represents the ent
Page 93 and 94: 3.4.2 Ensemble of Attention Scores
Page 95 and 96: Type Count Ratio (A) Correct 76 30.
Page 97 and 98: Minh-Thang Luong, Hieu Pham, and Ch
Page 99 and 100: Pair 1 Japanese [[ A ][ B ][ C ]
Page 101 and 102: ID n-grams len freq m1 prevent, | 1
Page 103 and 104: prediction accuracy with respect to
Page 105 and 106: Reference: [ A To provide] [ B a to
Page 107 and 108: 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-
Page 147 and 148:
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
Page 153 and 154:
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
Page 163 and 164:
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
Page 175 and 176:
We performed the tests under two co
Page 177 and 178:
the decoded sentence and the refere
Page 179 and 180:
Kenneth Heafield. 2011. Kenlm: Fast
Page 181 and 182:
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
Page 189 and 190:
Character-based Decoding in Tree-to
Page 191 and 192:
where W s ∈ R d×d is a matrix an
Page 193 and 194:
Model BLEU (BP) RIBES Character-bas
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The word-based NMT models cannot us
Page 197 and 198:
Razvan Pascanu, Tomas Mikolov, and
Page 199 and 200:
governs the grammaticality of the t
Page 201 and 202:
2.1 Quantization and Binarization o
Page 203 and 204:
3 Results Team Other Resources Syst
Page 205 and 206:
Peter F Brown, John Cocke, Stephen
Page 207 and 208:
Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210:
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
Page 215 and 216:
6 Conclusion and Future Work In thi
Page 217 and 218:
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
Page 223 and 224:
controlling the sentence length wit
Page 225 and 226:
Chinese-to-Japanese Patent Machine
Page 227 and 228:
using the reordering oracles over t
Page 229 and 230:
Conference on Natural Language Proc
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Figure 1: Hierarchical approach wit
Page 233 and 234:
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
Page 239 and 240:
2.2 Generalized Minimum Bayes Risk
Page 241 and 242:
5.0 4.5 4.0 Baseline model Enlarged
Page 243:
Felix Hill, Kyunghyun Cho, Sebastie
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December 11-16 2016 Osaka Japan

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