December 11-16 2016 Osaka Japan

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Durrani, N., Fraser, A. M., Schmid, H., Hoang, H., & Koehn, P. 2013. “ Can markov models over minimal translation units help phrase-based smt?.” In conference of the Association of Computational Linguistics . Gao, Qin, and Stephan Vogel. 2008. "Parallel implementations of word alignment tool." Software Engineering, Testing, and Quality Assurance for Natural Language Processing. Association for Computational Linguistics. Hermanto, Andi, Teguh Bharata Adji, and Noor Akhmad Setiawan. 2015. "Recurrent neural network language model for English-Indonesian Machine Translation: Experimental study." 2015 International Conference on Science in Information Technology (ICSITech). IEEE. Koehn, Philipp, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan. 2007. "Moses: Open source toolkit for statistical machine translation." In Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions. Association for Computational Linguistics. Larasati, Septina Dian. 2012. "Towards an Indonesian-English SMT system: A case study of an understudied and under-resourced language, Indonesian." WDS’12 Proceedings of Contributed Papers 1. Mantoro, Teddy, Jelita Asian, Riza Octavian, and Media Anugerah Ayu. 2013. "Optimal translation of English to Bahasa Indonesia using statistical machine translation system." In Information and Communication Technology for the Muslim World (ICT4M), 2013 5th International Conference of IEEE. Nakazawa, Toshiaki and Mino, Hideya and Ding, Chenchen and Goto, Isao and Neubig, Graham and Kurohashi, Sadao and Sumita, Eiichiro. 2016. “Overview of the 3rd Workshop on Asian Translation.” Proceedings of the 3rd Workshop on Asian Translation (WAT2016), October. Niehues, Jan, Teresa Herrmann, Stephan Vogel, and Alex Waibel. 2011. "Wider context by using bilingual language models in machine translation." InProceedings of the Sixth Workshop on Statistical Machine Translation. Association for Computational Linguistics. Schwenk, Holger. 2012. "Continuous Space Translation Models for Phrase-Based Statistical Machine Translation." COLING (Posters). Stolcke, Andreas. 2002. "SRILM-an extensible language modeling toolkit." Interspeech. Vol. 2002. Vaswani, Ashish, Yinggong Zhao, Victoria Fossum, and David Chiang. 2013. "Decoding with Large- Scale Neural Language Models Improves Translation." In EMNLP. Yulianti, Evi, Indra Budi, Achmad N. Hidayanto, Hisar M. Manurung, and Mirna Adriani. 2011. "Developing Indonesian-English Hybrid Machine Translation System." In Advanced Computer Science and Information System (ICACSIS), 2011 International Conference of IEEE. 74
Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation Kazuma Hashimoto, Akiko Eriguchi, and Yoshimasa Tsuruoka The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan {hassy, eriguchi, tsuruoka}@logos.t.u-tokyo.ac.jp Abstract This paper describes our UT-KAY system that participated in the Workshop on Asian Translation 2016. Based on an Attention-based Neural Machine Translation (ANMT) model, we build our system by incorporating a domain adaptation method for multiple domains and an attention-based unknown word replacement method. In experiments, we verify that the attention-based unknown word replacement method is effective in improving translation scores in Chinese-to-Japanese machine translation. We further show results of manual analysis on the replaced unknown words. 1 Introduction End-to-end Neural Machine Translation (NMT) with Recurrent Neural Networks (RNNs) is attracting increasing attention (Sutskever et al., 2014). By incorporating attention mechanisms (Bahdanau et al., 2015), NMT models have achieved state-of-the-art results on several translation tasks, such as Englishto-German (Luong et al., 2015a) and English-to-Japanese (Eriguchi et al., 2016) tasks. Although NMT is attractive due to its translation quality and relatively simple architecture, it is known to have some serious problems including unknown (or rare) word problems (Luong et al., 2015b). Thus, there is still room for improvement in many aspects of NMT models. In our UT-KAY system that participated in the Workshop on Asian Translation 2016 (WAT 2016) (Nakazawa et al., 2016a), we investigate the following two issues: • adaptation with multiple domains, and • attention-based unknown word replacement. Our system is based on an Attention-based NMT (ANMT) model (Luong et al., 2015a). To explicitly treat translation pairs from multiple domains, our system extends a domain adaptation method for neural networks (Watanabe et al., 2016), and apply it to the baseline ANMT model. To address the unknown word problems in translated sentences, we investigate the effectiveness of replacing each unknown word according to the attention scores output by the baseline ANMT model. In experiments, we apply our system to a Chinese-to-Japanese translation task of scientific text. Our experimental results show that the attention-based unknown word replacement method consistently improves the BLEU scores by about 1.0 for the baseline system, the domain adaptation system, and the ensemble of the two systems. Moreover, our manual analysis on the replaced unknown words indicates that the scores can be further improved if a high quality dictionary is available. While the domain adaptation method does not improve upon the baseline system in terms of the automatic evaluation metrics, the ensemble of the systems with and without the domain adaptation method boosts the BLEU score by 2.7. As a result, our UT-KAY system has been selected as one of the top three systems in the Chineseto-Japanese task at WAT 2016. 2 Related Work 2.1 Domain Adaptation for Sentence Generation Tasks An NMT model is usually built as a single large neural network and trained using a large parallel corpus. Such a parallel corpus is, in general, constructed by collecting sentence pairs from a variety of domains 75 Proceedings of the 3rd Workshop on Asian Translation, pages 75–83, 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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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
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: distribution as received in JPO ade
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
Page 111 and 112: Figure 2: An NRM considering phrase
Page 113 and 114: Mono Swap D right D left Acc. The r
Page 115 and 116: Method ja-en en-ja BLEU RIBES WER B
Page 117 and 118: Peng Li, Yang Liu, and Maosong Sun.
Page 119 and 120: Figure 1: The framework of NMT. Whe
Page 121 and 122: For long sentence, we discarded all
Page 123 and 124: As shown in the above tables and fi
Page 125 and 126: 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)
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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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