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System JPO Adequacy Pairwise BLEU RIBES AMFM PBMT w/o pre-ordering n/a n/a 0.3903 0.8057 0.7203 PBMT w/pre-ordering n/a 39.250 0.4075 0.8260 0.7302 PBMT w/pre-ordering (2015/rule-based) n/a n/a 0.4060 0.8234 n/a PBMT w/pre-ordering (2015/data-driven) n/a n/a 0.3977 0.8163 n/a NMT w/o pre-ordering n/a n/a 0.4499 0.8530 0.7522 NMT w/pre-ordering 3.44 46.500 0.4347 0.8453 0.7493 JAPIO PBMT w/pre-ordering† 3.24 46.250 0.4432 0.8350 0.7512 NICT PBMT w/pre-ordering† 3.23 43.250 0.4187 0.8296 0.7399 NICT PBMT w/pre-ordering n/a 36.750 0.4109 0.8270 0.7330 Table 1: Official evaluation results in JPO Adequacy, Pairwise Crowdsourcing Evaluation scores (Pairwise), BLEU, RIBES, and AMFM. Automatic evaluation scores are based on JUMAN Japanese word segmentation. Scores in bold are best in the same group. †: Systems used external resources. Language ppl. (dev) ppl. (devtest) ppl. (test) Chinese 185.573 211.370 220.821 Pre-ordered Chinese 203.639 231.218 240.533 Table 2: Source-side test set perplexities on dev, devtest, and test sets by word 5-gram language models of Chinese and pre-ordered Chinese. The vocabulary size is 172,108. increased the test set perplexity in the source language (Chinese) by about 10% (Table 2). Since this time we do not have human evaluation results of the baseline neural MT, we cannot evaluate the actual influence of pre-ordering in the neural MT for human understanding. This issue needs further analysis and investigation. 6 Conclusion This paper presented our pre-ordering-based system for a Chinese-to-Japanese patent MT for the WAT 2016 evaluation campaign. Our results showed that pre-ordering worked effectively with a phrase-based MT but not with a neural MT. The neural MT surprisingly improved the translation performance without any careful tuning. Its result was competitive with a highly tuned phrase-based MT system. Acknowledgments We greatly appreciate the workshop organizers for this valuable evaluation campaign. We also thank the Japan Patent Office for providing its patent translation dataset. References Sho Hoshino, Yusuke Miyao, Katsuhito Sudoh, Katsuhiko Hayashi, and Masaaki Nagata. 2015. Discriminative Preordering Meets Kendall’s τ Maximization. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 139–144. Laura Jehl, Adrià de Gispert, Mark Hopkins, and Bill Byrne. 2014. Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 239–248. Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412–1421, Lisbon, Portugal, September. Association for Computational Linguistics. Tetsuji Nakagawa. 2015. Efficient top-down btg parsing for machine translation preordering. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint 214
Conference on Natural Language Processing (Volume 1: Long Papers), pages 208–218, Beijing, China, July. Association for Computational Linguistics. Toshiaki Nakazawa, Hideya Mino, Chenchen Ding, Isao Goto, Graham Neubig, Sadao Kurohashi, and Eiichiro Sumita. 2016. Overview of the 3rd Workshop on Asian Translation. In Proceeding of the 3rd Workshop on Asian Translation (WAT2016), Osaka, Japan, December. Katsuhito Sudoh and Masaaki Nagata. 2015. Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2015. In Proceedings of the 2nd Workshop on Asian Translation (WAT2015), pages 95–98. Katsuhito Sudoh, Jun Suzuki, Yasuhiro Akiba, Hajime Tsukada, and Masaaki Nagata. 2014. An English/Chinese/Korean-to-Japanese Statistical Machine Translation System for Patent Documents. In Proceedings of the 20th Annual Meeting of the Association for Natural Language Processing, pages 606–609. (in Japanese; ). Jun Suzuki, Hideki Isozaki, Xavier Carreras, and Michael Collins. 2009. An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 551–560. Jun Suzuki, Kevin Duh, and Masaaki Nagata. 2012. Joint Natural Language Analysis using Augmented Lagrangian. In Proceedings of the 18th Annual Meeting of the Association for Natural Language Processing, pages 1284–1287. (in Japanese; Duh, ). Nan Yang, Mu Li, Dongdong Zhang, and Nenghai Yu. 2012. A Ranking-based Approach to Word Reordering for Statistical Machine Translation. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 912–920. 215
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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
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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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Page 179 and 180: Kenneth Heafield. 2011. Kenlm: Fast
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Page 185 and 186: 3.6 Ensembling Ensembling has previ
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Page 207 and 208: Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210: Language Sentence Chinese 该 / 钽
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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
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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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