December 11-16 2016 Osaka Japan

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Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016 Graham Neubig †∗ † Nara Institute of Science and Technology, Japan ∗ Carnegie Mellon University, USA gneubig@cs.cmu.edu Abstract This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian Translation was based on attentional neural machine translation (NMT) models. In addition to the standard NMT model, we make a number of improvements, most notably the use of discrete translation lexicons to improve probability estimates, and the use of minimum risk training to optimize the MT system for BLEU score. As a result, our system achieved the highest translation evaluation scores for the task. 1 Introduction Neural machine translation (NMT; (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014)), creation of translation models using neural networks, has quickly achieved state-of-the-art results on a number of translation tasks (Luong and Manning, 2015; Sennrich et al., 2016a). In this paper, we describe NMT systems for the Japanese-English scientific paper translation task of the Workshop on Asian Translation (WAT) 2016 (Nakazawa et al., 2016a). The systems are built using attentional neural networks (Bahdanau et al., 2015; Luong et al., 2015), with a number of improvements (§2). In particular we focus on two. First, we follow the recent work of Arthur et al. (2016) in incorporating discrete translation lexicons to improve the probability estimates of the neural translation model (§3). Second, we incorporate minimum-risk training (Shen et al., 2016) to optimize the parameters of the model to improve translation accuracy (§4). In experiments (§5), we examine the effect of each of these improvements, and find that they both contribute to overall translation accuracy, leading to state-of-the-art results on the Japanese-English translation task. 2 Baseline Neural Machine Translation Model Our baseline translation model is the attentional model implemented in the lamtram toolkit (Neubig, 2015), which is a combination of the models of Bahdanau et al. (2015) and Luong et al. (2015) that we found to be effective. We describe the model briefly here for completeness, and refer readers to the previous papers for a more complete description. 2.1 Model Structure Our model creates a model of target sentence E = e |E| 1 given source sentence F = f |F | 1 . These words belong to the source vocabulary V f , and the target vocabulary V e respectively. NMT performs this translation by calculating the conditional probability p m (e i |F, e i−1 1 ) of the ith target word e i based on the source F and the preceding target words e i−1 1 . This is done by encoding the context ⟨F, e1 i−1 ⟩ as a fixed-width vector η i , and calculating the probability as follows: p m (e i |F, e i−1 1 ) = softmax(W s η i + b s ), (1) where W s and b s are respectively weight matrix and bias vector parameters. The exact variety of the NMT model depends on how we calculate η i used as input, and as mentioned above, in this case we use an attentional model. 119 Proceedings of the 3rd Workshop on Asian Translation, pages 119–125, 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
Page 81 and 82: Kenneth Heafield. 2011. Kenlm: Fast
Page 83 and 84: under-studied. MorphInd, a morphana
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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
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: former system can translate more th
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
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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
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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 and 178: the decoded sentence and the refere
Page 179 and 180: Kenneth Heafield. 2011. Kenlm: Fast
Page 181 and 182: 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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