December 11-16 2016 Osaka Japan

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Success translation Reference i ’m in a hurry . Source(EC) *pro* NO EC are in a hurry . ECs i ’m in a hurry . Reference how much to rent it for three days ? Source *pro* Reordered Source *pro* NO ECs i have a three days and how much will it be ? ECs i have a three days and how much will it be ? Pre-ordered w/o EC what would you like to hire and three days . Pre-ordered w/ EC how much will it cost to three days ? Failed translation Reference do you have any fruits or plants ? Source *pro* Reordered Source *pro* NO ECs i have a carrying any plants and fruits ? ECs i have fruit or plant ? Pre-ordered w/o EC do you have some fruit or plants ? Pre-ordered w/ EC i have a carrying any plants and fruits ? Table 3: Translation Examples 5 Conclusion In this paper, we propose a method to integrate empty category detection into preordering-based machine translation system. We examined the effect of empty category detection on both the IWSLT and KFTT datasets. We showed by experiments that empty category detection results in an improvement in machine translation, in particular for the IWSLT dataset, which is a spoken language corpus. We also showed that, by using preordering with empty categories, we were able to achieve consistent improvement in translation accuracy for the KFTT dataset. In future, we would like to improve the filtering strategy for empty categories. The integration of empty categories into machine translation is problematic in that empty categories are inserted excessively. There are some empty categories that are not aligned to any words in the target language. In this work, we simply filtered these empty categories based on the probability to alleviate the problem. However, for addressing this problem more appropriately, we should consider both source language context and target language context. We expect the corpus-based approach such as Wang et al., (2016) address this problem. References Tagyoung Chung and Daniel Gildea. 2010. Effects of Empty Categories on Machine Translation. In Proceedings of the EMNLP2010, pages 636–645. Matthias Eck and Chiori Hori. 2005. Overview of the iwslt 2005 evaluation campaign. In Proceedings of the IWSLT2005, pages 1–22. Masatugu Hangyo, Daisuke Kawahara, and Sadao Kurohashi. 2013. Japanese Zero Reference Resolution Considering Zero Exophora and Author/Reader Mentions. In Proceedings of the EMNLP2013, pages 924–934. Katsuhiko Hayashi, Jun Suzuki, and Masaaki Nagata. 2016. Shift-reduce spinal tag parsing with dynamic programming. Transactions of the Japanese Society for Artificial Intelligence, 31(2):1–8. 164
Kenneth Heafield. 2011. Kenlm: Faster and smaller language model queries. In Proceedings of the Sixth Workshop on Statistical Machine Translation, pages 187–197. Sho Hoshino, Yusuke Miyao, Katsuhito Sudoh, Katsuhiko Hayashi, and Masaaki Nagata. 2015. Discriminative preordering meets kendall s tau maximization. In Proceedings of the ACL-IJCNLP2015, pages 139–144. Ryu Iida, Kentaro Inui, and Yuji Matsumoto. 2007. Zero-anaphora resolution by learning rich syntactic pattern features. ACM Transactions on Asian Language Information Processing, 6(4):1–22. 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 EMNLP2010, pages 944–952. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the ACL2007, pages 177–180. Taku Kudo, Hiroshi Ichikawa, and Hideto Kazawa. 2014. A joint inference of deep case analysis and zero subject generation for Japanese-to-English statistical machine translation. In Proceedings of the ACL2014, pages 557– 562. Hiromi Nakaiwa and Satoru Ikehara. 1995. Intrasentential Resolution of Japanese Zero Pronouns using Pragmatic and Semantic Constraints. In Proceeedings of the International Conference on Theoretical and Methodological Issues in Machine Translation, pages 96–105. Graham Neubig. 2011. The Kyoto free translation task. http://www.phontron.com/kftt. Franz Josef Och. 2007. Minimum error rate training in statistical machine translation. In Proceedings of the ACL2007, pages 160–167. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the ACL2002, pages 311–318. Hirotoshi Taira, Katsuhito Sudoh, and Masaaki Nagata. 2012. Zero pronoun resolution can improve the quality of je translation. In Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 111–118. Shunsuke Takeno, Nagata Masaaki, and Kazuhide Yamamoto. 2015. Empty Category Detection using Path Features and Distributed Case Frames. In Proceedings of the EMNLP2015, pages 1335–1340. Kiyotaka Uchimoto and Yasuharu Den. 2008. Word-level dependency-structure annotation to corpus of spontaneous japanese and its application. In Proceedings of the LREC2008, pages 3118–3122. Longyue Wang, Zhaopeng Tu, Xiaojun Zhang, Hang Li, Andy Way, and Qun Liu. 2016. A Novel Approach to Dropped Pronoun Translation. In Proceedings of the NAACL-HLT2016, pages 983–993. Bing Xiang, Xiaoqiang Luo, and Bowen Zhou. 2013. Enlisting the ghost: Modeling empty categories for machine translation. In Proceedings of the ACL2013, pages 822–831. 165
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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
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
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: the decoded sentence and the refere
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 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
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
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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
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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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