December 11-16 2016 Osaka Japan

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Toshiaki Nakazawa, Manabu Yaguchi, Kiyotaka Uchimoto, Masao Utiyama, Eiichiro Sumita, Sadao Kurohashi, and Hitoshi Isahara. 2016b. ASPEC: Asian scientific paper excerpt corpus. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2016), pages 2204–2208. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on Association for Computational Linguistics (ACL), pages 311–318. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Controlling politeness in neural machine translation via side constraints. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 35–40. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems 27, pages 3104–3112. Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton. 2015. Chainer: a next-generation open source framework for deep learning. In Proceedings of Workshop on Machine Learning Systems (LearningSys) in the 2015 Conference on Neural Information Processing Systems (NIPS). 210
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016 Katsuhito Sudoh and Masaaki Nagata Communication Science Laboratories, NTT Corporation 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan sudoh.katsuhito@lab.ntt.co.jp Abstract This paper presents our Chinese-to-Japanese patent machine translation system for WAT 2016 (Group ID: ntt) that uses syntactic pre-ordering over Chinese dependency structures. Chinese words are reordered by a learning-to-rank model based on pairwise classification to obtain word order close to Japanese. In this year’s system, two different machine translation methods are compared: traditional phrase-based statistical machine translation and recent sequence-to-sequence neural machine translation with an attention mechanism. Our pre-ordering showed a significant improvement over the phrase-based baseline, but, in contrast, it degraded the neural machine translation baseline. 1 Introduction Patent documents, which are well-structured written texts that describe the technical details of inventions, are expected to have almost no semantic ambiguities caused by indirect or rhetorical expressions. Therefore, they are good candidates for literal translation, which most machine translation (MT) approaches aim to do. One technical challenge for patent machine translation is the complex syntactic structure of patent documents, which typically have long sentences that complicate MT reordering, especially for word order in distant languages. Chinese and Japanese have similar word order in noun modifiers but different subject-verb-object order, requiring long distance reordering in translation. In the WAT 2016 evaluation campaign (Nakazawa et al., 2016), we participated in a Chinese-to-Japanese patent translation task and tackled long distance reordering by syntactic pre-ordering based on Chinese dependency structures, as in our last year’s system (Sudoh and Nagata, 2015). We also use a recent neural MT as the following MT implementation for comparison with a traditional phrase-based statistical MT. Our system basically consists of three components: Chinese syntactic analysis (word segmentation, part-of-speech (POS) tagging, and dependency parsing) adapted to patent documents; dependency-based syntactic pre-ordering with hand-written rules or a learning-to-rank model; and the following MT component (phrase-based MT or neural MT). This paper describes our system’s details and discusses our evaluation results. 2 System Overview Figure 1 shows a brief workflow of our Chinese-to-Japanese MT system. Its basic architecture is standard with syntactic pre-ordering. Input sentences are first applied to word segmentation and POS tagging, parsed into dependency trees, reordered using pre-ordering rules or a pre-ordering model, and finally translated into Japanese by MT. 3 Chinese Syntactic Analysis: Word Segmentation, Part-of-Speech Tagging, and Dependency Parsing Word segmentation and POS tagging are solved jointly (Suzuki et al., 2012) for better Chinese word segmentation based on POS tag sequences. The dependency parser produces untyped dependency trees. The 211 Proceedings of the 3rd Workshop on Asian Translation, pages 211–215, Osaka, Japan, December 11-17 2016.
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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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Page 179 and 180: Kenneth Heafield. 2011. Kenlm: Fast
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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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