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It worked fairly well for some language pairs with similar word order such as English-French but is not appropriate for distant language pairs including Japanese-English. Lexicalized reordering model (LRM) (Tillmann, 2004; Koehn et al., 2007; Galley and Manning, 2008) introduced lexical constraints of the phrase reordering and not just penalizing long-distance reordering. Pre-ordering methods can be divided into two types: (1) Rule-based preprocessing methods (Collins et al., 2005; Isozaki et al., 2010b) parse source sentences and reorder the words using hand-crafted rules. (2) Discriminative pre-ordering models (Tromble and Eisner, 2009; Wu et al., 2011; Hoshino et al., 2015; Nakagawa, 2015) learn whether children of each node should be reordered using (automatically) aligned parallel corpus. However, pre-ordering models cannot use the target language information in decoding. Therefore, optimizing phrase ordering using target-side features like phrase translation probability and word alignment is not possible, as done in our proposed method. Post-ordering methods (Sudoh et al., 2011; Goto et al., 2012) are sometimes used in Japanese-to- English translation. They first translate Japanese input into head final English texts, then reorder head final English texts into English word orders. Post-ordering methods have the advantage of being able to use syntactic features at low computational cost, but need an accurate parser on the target side. 7 Conclusion In this study, we improved a neural reordering model in PBSMT using phrase translation and word alignment. We proposed phrase translation and word alignment features to construct phrase vectors. The experimental results demonstrate that our proposed method improves the accuracy of phrase reordering. In addition, we showed that the proposed method was effective when phrase pairs included the NULL alignment. Evaluation on Japanese-to-English and English-to-Japanese translations indicated that the proposed method does not exhibits significant improvements in BLEU compared with those of the neural reordering model (Li et al., 2014). In the future, we plan to integrate our reordering model into attentional neural machine translations. Acknowledgments We thank the anonymous reviewers for their helpful comments on earlier versions. We also thank Yui Suzuki and Aizhan Imankulova for their annotation. References Michael Collins, Philipp Koehn, and Ivona Kucerova. 2005. Clause restructuring for statistical machine translation. In ACL. Yiming Cui, Shijin Wang, and Jianfeng Li. 2016. Lstm neural reordering feature for statistical machine translation. In NAACL. Michel Galley and Christopher D Manning. 2008. A simple and effective hierarchical phrase reordering model. In EMNLP. Isao Goto, Masao Utiyama, and Eiichiro Sumita. 2012. Post-ordering by parsing for Japanese-English statistical machine translation. In ACL. Sho Hoshino, Yusuke Miyao, Katsuhito Sudoh, Katsuhiko Hayashi, and Masaaki Nagata. 2015. Discriminative preordering meets Kendall’s τ maximization. In ACL-IJCNLP. Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010a. Automatic evaluation of translation quality for distant language pairs. In EMNLP. Hideki Isozaki, Katsuhito Sudoh, Hajime Tsukada, and Kevin Duh. 2010b. Head finalization: A simple reordering rule for SOV languages. In WMT. Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In NAACL-HLT. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, and Richard Zens. 2007. Moses: Open source toolkit for statistical machine translation. In ACL (demo). 102
Peng Li, Yang Liu, and Maosong Sun. 2013. Recursive autoencoders for ITG-based translation. In EMNLP. Peng Li, Yang Liu, Maosong Sun, Tatsuya Izuha, and Dakun Zhang. 2014. A neural reordering model for phrasebased translation. In COLING. Masaaki Nagata, Kuniko Saito, Kazuhide Yamamoto, and Kazuteru Ohashi. 2006. A clustered global phrase reordering model for statistical machine translation. In COLING-ACL. Tetsuji Nakagawa. 2015. Efficient top-down BTG parsing for machine translation preordering. In ACL-IJCNLP. Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1). Franz Josef Och. 2003. Minimum error rate training in statistical machine translation. In ACL. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In ACL. Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semisupervised recursive autoencoders for predicting sentiment distributions. In EMNLP. Katsuhito Sudoh, Xianchao Wu, Kevin Duh, Hajime Tsukada, and Masaaki Nagata. 2011. Post-ordering in statistical machine translation. In MT Summit. Christoph Tillmann. 2004. A unigram orientation model for statistical machine translation. In HLT-NAACL. Seiya Tokui, Kenta Oono, and Shohei Hido. 2015. Chainer: a next-generation open source framework for deep learning. In NIPS Workshop on Machine Learning Systems. Roy Tromble and Jason Eisner. 2009. Learning linear ordering problems for better translation. In EMNLP. Xianchao Wu, Katsuhito Sudoh, Kevin Duh, Hajime Tsukada, and Masaaki Nagata. 2011. Extracting pre-ordering rules from predicate-argument structures. In IJCNLP. 103
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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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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
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
Page 109 and 110: Therefore, we propose to use phrase
Page 111 and 112: Figure 2: An NRM considering phrase
Page 113 and 114: Mono Swap D right D left Acc. The r
Page 115: Method ja-en en-ja BLEU RIBES WER B
Page 119 and 120: Figure 1: The framework of NMT. Whe
Page 121 and 122: For long sentence, we discarded all
Page 123 and 124: As shown in the above tables and fi
Page 125 and 126: Translation systems and experimenta
Page 127 and 128: The number of TM training sentence
Page 129 and 130: where, f, p and e means a source, p
Page 131 and 132: former system can translate more th
Page 133 and 134: Lexicons and Minimum Risk Training
Page 135 and 136: 2.2 Parameter Optimization If we de
Page 137 and 138: ML (λ=0.0) ML (λ=0.8) MR (λ=0.0)
Page 139 and 140: Graham Neubig. 2013. Travatar: A fo
Page 141 and 142: Feature Space Common (h ) Domain 1
Page 143 and 144: Preprocessing Training Translation
Page 145 and 146: JPC ASPEC LM Method Ja-En En-Ja Ja-
Page 147 and 148: Translation Using JAPIO Patent Corp
Page 149 and 150: 4 Results Table 1 shows official ev
Page 151 and 152: Table 3: Examples of translation er
Page 153 and 154: 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
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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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