December 11-16 2016 Osaka Japan

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Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, and Mamoru Komachi. 2016. Controlling the voice of a sentence in japanese-to-english neural machine translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 203–210, Osaka, Japan, December. The COLING 2016 Organizing Committee. Wei Yang and Yves Lepage. 2016. Improving patent translation using bilingual term extraction and re-tokenization for chinese–japanese. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 194–202, Osaka, Japan, December. The COLING 2016 Organizing Committee. 46
Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation Zi Long Takehito Utsuro Grad. Sc. Sys. & Inf. Eng., University of Tsukuba, sukuba, 305-8573, Japan Tomoharu Miitsuhashi Japan Patent Information Organization, 4-1-7, Tokyo, Koto-ku, Tokyo, 135-0016, Japan Mikio Yamamoto Grad. Sc. Sys. & Inf. Eng., University of Tsukuba, Tsukuba, 305-8573, Japan Abstract Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, NMT cannot handle a larger vocabulary because training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. In NMTs, words that are out of vocabulary are represented by a single unknown token. In this paper, we propose a method that enables NMT to translate patent sentences comprising a large vocabulary of technical terms. We train an NMT system on bilingual data wherein technical terms are replaced with technical term tokens; this allows it to translate most of the source sentences except technical terms. Further, we use it as a decoder to translate source sentences with technical term tokens and replace the tokens with technical term translations using SMT. We also use it to rerank the 1,000-best SMT translations on the basis of the average of the SMT score and that of the NMT rescoring of the translated sentences with technical term tokens. Our experiments on Japanese-Chinese patent sentences show that the proposed NMT system achieves a substantial improvement of up to 3.1 BLEU points and 2.3 RIBES points over traditional SMT systems and an improvement of approximately 0.6 BLEU points and 0.8 RIBES points over an equivalent NMT system without our proposed technique. 1 Introduction Neural machine translation (NMT), a new approach to solving machine translation, has achieved promising results (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Cho et al., 2014; Bahdanau et al., 2015; Jean et al., 2014; Luong et al., 2015a; Luong et al., 2015b). An NMT system builds a simple large neural network that reads the entire input source sentence and generates an output translation. The entire neural network is jointly trained to maximize the conditional probability of a correct translation of a source sentence with a bilingual corpus. Although NMT offers many advantages over traditional phrase-based approaches, such as a small memory footprint and simple decoder implementation, conventional NMT is limited when it comes to larger vocabularies. This is because the training complexity and decoding complexity proportionally increase with the number of target words. Words that are out of vocabulary are represented by a single unknown token in translations, as illustrated in Figure 1. The problem becomes more serious when translating patent documents, which contain several newly introduced technical terms. There have been a number of related studies that address the vocabulary limitation of NMT systems. Jean el al. (2014) provided an efficient approximation to the softmax to accommodate a very large vocabulary in an NMT system. Luong et al. (2015b) proposed annotating the occurrences of a target unknown word token with positional information to track its alignments, after which they replace the tokens with their translations using simple word dictionary lookup or identity copy. Li et al. (2016) proposed to replace out-of-vocabulary words with similar in-vocabulary words based on a similarity model learnt from monolingual data. Sennrich et al. (2016) introduced an effective approach based on encoding rare and unknown words as sequences of subword units. Luong and Manning (2016) provided a character-level 47 Proceedings of the 3rd Workshop on Asian Translation, pages 47–57, 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
Page 9 and 10: Table of Contents Overview of the 3
Page 11 and 12: Conference Program December 12, 201
Page 13 and 14: December 12, 2016 (continued) 12:00
Page 15 and 16: Overview of the 3rd Workshop on Asi
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: Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
Page 63 and 64: 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
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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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