December 11-16 2016 Osaka Japan

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Philipp Koehn. 2004. Pharaoh: a beam search decoder for phrase-based statistical machine translation models. In Machine translation: From real users to research, pages 115–124. Springer. Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of MT Summit, volume 5, pages 79–86. Philipp Koehn. 2010. An experimental management system. The Prague Bulletin of Mathematical Linguistics, 94:87–96. Daniel Marcu and William Wong. 2002. A phrase-based, joint probability model for statistical machine translation. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 133–139. Association for Computational Linguistics. Toshiaki Nakazawa, Hideya Mino, Isao Goto, Sadao Kurohashi, and Eiichiro Sumita. 2014. Overview of the first workshop on Asian translation. In Proceedings of the First Workshop on Asian1 Translation (WAT2014), Tokyo, Japan. Toshiaki Nakazawa, Hideya Mino, Isao Goto, Graham Neubig, Sadao Kurohashi, and Eiichiro Sumita. 2015. Overview of the 2nd workshop on Asian translation. In Proceedings of the 2nd Workshop on Asian Translation (WAT2015), Kyoto, Japan. Toshiaki Nakazawa, Hideya Mino, Isao Goto, Graham Neubig, Sadao Kurohashi, and Eiichiro Sumita. 2016. Overview of the 3rd workshop on Asian translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), Kyoto, Japan. Graham Neubig, Makoto Morishita, and Satoshi Nakamura. 2015. Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015. In Proceedings of the 2nd Workshop on Asian Translation (WAT2015), pages 35–41, Kyoto, Japan, October. Franz Josef Och and Hermann Ney. 2002. Discriminative training and maximum entropy models for statistical machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA., pages 295–302. Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational linguistics, 29(1):19–51. Franz Och. 1995. Maximum-Likelihood-Schätzung von Wortkategorien mit Verfahren der kombinatorischen Optimierung. Bachelor’s thesis (Studienarbeit), Universität Erlangen-Nürnburg. Franz Josef Och. 2003. Minimum error rate training in statistical machine translation. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, pages 160–167. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Edinburgh neural machine translation systems for wmt 16. In Proceedings of the First Conference on Machine Translation, pages 371–376, Berlin, Germany, August. Association for Computational Linguistics. Andreas Stolcke. 2002. Srilm-an extensible language modeling toolkit. In Seventh International Conference on Spoken Language Processing, pages 901–904. Liling Tan and Francis Bond. 2014. Manipulating input data in machine translation. In Proceedings of the 1st Workshop on Asian Translation (WAT2014). Liling Tan, Jon Dehdari, and Josef van Genabith. 2015a. An Awkward Disparity between BLEU / RIBES Scores and Human Judgements in Machine Translation. In Proceedings of the 2nd Workshop on Asian Translation (WAT2015), pages 74–81, Kyoto, Japan, October. Liling Tan, Josef van Genabith, and Francis Bond. 2015b. Passive and pervasive use of bilingual dictionary in statistical machine translation. In Proceedings of the Fourth Workshop on Hybrid Approaches to Translation (HyTra), pages 30–34, Beijing. Christoph Tillmann. 2004. A unigram orientation model for statistical machine translation. In Proceedings of HLT-NAACL 2004: Short Papers, pages 101–104. David Vilar, Daniel Stein, Matthias Huck, and Hermann Ney. 2010. Jane: Open source hierarchical translation, extended with reordering and lexicon models. In Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, pages 262–270. Jonathan Weese, Juri Ganitkevitch, Chris Callison-Burch, Matt Post, and Adam Lopez. 2011. Joshua 3.0: Syntax-based machine translation with the thrax grammar extractor. In Proceedings of the Sixth Workshop on Statistical Machine Translation, pages 478–484. Edward W. D. Whittaker and Bhiksha Raj. 2001. Quantization-based language model compression. In Proceedings of INTER- SPEECH, pages 33–36. 192
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, ukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. ArXiv e-prints, September. Richard Zens, Franz Josef Och, and Hermann Ney. 2002. Phrase-based statistical machine translation. In KI 2002: Advances in Artificial Intelligence, pages 18–32. Springer. Andreas Zollmann, Ashish Venugopal, Franz Och, and Jay Ponte. 2008. A systematic comparison of phrase-based, hierarchical and syntax-augmented statistical mt. In Proceedings of the 22nd International Conference on Computational Linguistics- Volume 1, pages 1145–1152. 193
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WAT 2016 The 3rd Workshop on Asian
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Preface Many Asian countries are ra
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Technical Collaborators Luis Fernan
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Table of Contents Overview of the 3
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Conference Program December 12, 201
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December 12, 2016 (continued) 12:00
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Overview of the 3rd Workshop on Asi
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LangPair Train Dev DevTest Test JPC
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ASPEC JPC IITB BPPT pivot System ID
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3.6 Tree-to-String Syntax-based SMT
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• Use of other resources in addit
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ASPEC JPC BPPT IITBC pivot Team ID
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ut the adequacy is worse. As for AS
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Figure 3: Official evaluation resul
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Figure 11: Official evaluation resu
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Figure 13: Official evaluation resu
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Annotator A Annotator B all weighte
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Kyoto-U 2 bjtu nlp UT-KAY 1 UT-KAY
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Online B IITP-MT EHR Online A ≫
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SYSTEM ID ID METHOD OTHER RESOURCES
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SYSTEM ID ID METHOD OTHER BLEU RIBE
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Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
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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
Page 155 and 156: #$ #%$ #$ !""
Page 157 and 158: This formula requires a context of
Page 159 and 160: Sentence Segmenter Parameters Dev.
Page 161 and 162: a meaningful baseline for related r
Page 163 and 164: Similar Southeast Asian Languages:
Page 165 and 166: τ around 0.71, and the Malay-Indon
Page 167 and 168: -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3
Page 169 and 170: 1.800 1.600 1.400 1.200 1.000 0.800
Page 171 and 172: Integrating empty category detectio
Page 173 and 174: 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
Page 183 and 184: proportionally slower to compute (a
Page 185 and 186: 3.6 Ensembling Ensembling has previ
Page 187 and 188: 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
Page 195 and 196: 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: Peter F Brown, John Cocke, Stephen
Page 209 and 210: Language Sentence Chinese 该 / 钽
Page 211 and 212: Chinese or Japanese sentences Chine
Page 213 and 214: 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
Page 219 and 220: Figure 2: Flow of the voice predict
Page 221 and 222: 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
Page 231 and 232: Figure 1: Hierarchical approach wit
Page 233 and 234: newswire test and development set o
Page 235 and 236: Source: the rain and cold wind on W
Page 237 and 238: 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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