December 11-16 2016 Osaka Japan

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December 12, 2016 (continued) 16:05–17:00 Poster presentation II (System description) Kyoto University Participation to WAT 2016 Fabien Cromieres, Chenhui Chu, Toshiaki Nakazawa and Sadao Kurohashi Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation Akiko Eriguchi, Kazuma Hashimoto and Yoshimasa Tsuruoka Faster and Lighter Phrase-based Machine Translation Baseline Liling Tan Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for Chinese–Japanese Wei Yang and Yves Lepage Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato and Mamoru Komachi Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016 Katsuhito Sudoh and Masaaki Nagata IITP English-Hindi Machine Translation System at WAT 2016 Sukanta Sen, Debajyoty Banik, Asif Ekbal and Pushpak Bhattacharyya Residual Stacking of RNNs for Neural Machine Translation Raphael Shu and Akiva Miura 17:00– Closing xiv
Overview of the 3rd Workshop on Asian Translation Toshiaki Nakazawa Chenchen Ding and Hideya Mino Japan Science and National Institute of Technology Agency Information and nakazawa@pa.jst.jp Communications Technology {chenchen.ding, hideya.mino}@nict.go.jp Isao Goto NHK goto.i-es@nhk.or.jp Graham Neubig Carnegie Mellon University gneubig@cs.cmu.edu Sadao Kurohashi Kyoto University kuro@i.kyoto-u.ac.jp Abstract This paper presents the results of the shared tasks from the 3rd workshop on Asian translation (WAT2016) including J↔E, J↔C scientific paper translation subtasks, C↔J, K↔J, E↔J patent translation subtasks, I↔E newswire subtasks and H↔E, H↔J mixed domain subtasks. For the WAT2016, 15 institutions participated in the shared tasks. About 500 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated. 1 Introduction The Workshop on Asian Translation (WAT) is a new open evaluation campaign focusing on Asian languages. Following the success of the previous workshops WAT2014 (Nakazawa et al., 2014) and WAT2015 (Nakazawa et al., 2015), WAT2016 brings together machine translation researchers and users to try, evaluate, share and discuss brand-new ideas of machine translation. We are working toward the practical use of machine translation among all Asian countries. For the 3rd WAT, we adopt new translation subtasks with English-Japanese patent description, Indonesian-English news description and Hindi-English and Hindi-Japanese mixed domain corpus in addition to the subtasks that were conducted in WAT2015. Furthermore, we invited research papers on topics related to the machine translation, especially for Asian languages. The submissions of the research papers were peer reviewed by at least 2 program committee members and the program committee accepted 7 papers that cover wide variety of topics such as neural machine translation, simultaneous interpretation, southeast Asian languages and so on. WAT is unique for the following reasons: • Open innovation platform The test data is fixed and open, so evaluations can be repeated on the same data set to confirm changes in translation accuracy over time. WAT has no deadline for automatic translation quality evaluation (continuous evaluation), so translation results can be submitted at any time. • Domain and language pairs WAT is the world’s first workshop that uses scientific papers as the domain, and Chinese ↔ Japanese, Korean ↔ Japanese and Indonesian ↔ English as language pairs. In the future, we will add more Asian languages, such as Vietnamese, Thai, Burmese and so on. • Evaluation method Evaluation is done both automatically and manually. For human evaluation, WAT uses pairwise evaluation as the first-stage evaluation. Also, JPO adequacy evaluation is conducted for the selected submissions according to the pairwise evaluation results. This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/ 1 Proceedings of the 3rd Workshop on Asian Translation, pages 1–46, Osaka, Japan, December 11-17 2016.
Page 1 and 2: WAT 2016 The 3rd Workshop on Asian
Page 3: Preface Many Asian countries are ra
Page 6 and 7: Technical Collaborators Luis Fernan
Page 9 and 10: Table of Contents Overview of the 3
Page 11 and 12: Conference Program December 12, 201
Page 13: December 12, 2016 (continued) 12:00
Page 17 and 18: LangPair Train Dev DevTest Test JPC
Page 19 and 20: ASPEC JPC IITB BPPT pivot System ID
Page 21 and 22: 3.6 Tree-to-String Syntax-based SMT
Page 23 and 24: • Use of other resources in addit
Page 25 and 26: ASPEC JPC BPPT IITBC pivot Team ID
Page 27 and 28: ut the adequacy is worse. As for AS
Page 29 and 30: Figure 3: Official evaluation resul
Page 37 and 38: Figure 11: Official evaluation resu
Page 39 and 40: Figure 13: Official evaluation resu
Page 41 and 42: Annotator A Annotator B all weighte
Page 43 and 44: Kyoto-U 2 bjtu nlp UT-KAY 1 UT-KAY
Page 45 and 46: Online B IITP-MT EHR Online A ≫
Page 47 and 48: SYSTEM ID ID METHOD OTHER RESOURCES
Page 49 and 50: SYSTEM ID ID METHOD OTHER BLEU RIBE
Page 59 and 60: Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
Page 61 and 62: Translation of Patent Sentences wit
Page 63 and 64: 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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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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