December 11-16 2016 Osaka Japan

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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 Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS). Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016. Modeling coverage for neural machine translation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 76–85, Berlin, Germany, August. Association for Computational Linguistics. Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329. Matthew D Zeiler. 2012. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701. 174
Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan {eriguchi, hassy, tsuruoka}@logos.t.u-tokyo.ac.jp Abstract This paper reports our systems (UT-AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT’16) and their results in the English-to-Japanese translation task. Our model is based on the tree-to-sequence Attention-based NMT (ANMT) model proposed by Eriguchi et al. (2016). We submitted two ANMT systems: one with a word-based decoder and the other with a character-based decoder. Experimenting on the English-to-Japanese translation task, we have confirmed that the character-based decoder can cover almost the full vocabulary in the target language and generate translations much faster than the word-based model. 1 Introduction End-to-end Neural Machine Translation (NMT) models have recently achieved state-of-the-art results in several translation tasks (Luong et al., 2015a; Luong et al., 2015b). Those NMT models are based on the idea of sequence-to-sequence learning (Sutskever et al., 2014), where both of the source and the target sentences are considered as a sequence of symbols (e.g. words or characters) and they are directly converted via a vector space. The sequence of symbols on the source side is input into a vector space, and the sequence of symbols on the target side is output from the vector space. In the end-to-end NMT models, the above architectures are embodied by a single neural network. The optimal unit for NMT is an important research question discussed in the community. Early NMT models employ a word as a unit of the sequence (Cho et al., 2014b; Sutskever et al., 2014). Sennrich et al. (2016) have used a Byte-Pair Encoding (BPE) method to create a sub-word level vocabulary according to the frequencies of sub-word appearance in the corpus. They successfully replaced a large word vocabulary in German and Russian with a much smaller sub-word vocabulary. They have also shown that their sub-word-based NMT model gives better translations than the word-based NMT models. The smallest unit of a sequence of text data is a character. The character-based approach has attracted much attention in the field of NMT, because it enables an NMT model to handle all of the tokens in the corpus (Costa-jussà and Fonollosa, 2016; Chung et al., 2016). A hybrid model of the word-based and the character-based model has also been proposed by Luong and Manning (2016). These studies reported the success and effectiveness in translating the out-of-vocabulary words. In this paper, we apply character-based decoding to a tree-based NMT model (Eriguchi et al., 2016). The existing character-based models focus only on the sequence-based NMT models. The objective of this paper is to analyze the results of the character-based decoding in the tree-based NMT model. We also enrich the tree-based encoder with syntactic features. Figure 1 shows an overview of our system. We conducted the English-to-Japanese translation task on the WAT’16 dataset. The results of our characterbased decoder model show that its translation accuracy is lower than that of the word-based decoder model by 1.34 BLEU scores, but the character-based decoder model needed much less time to generate a sentence. 2 Neural Machine Translation End-to-end NMT models have recently outperformed phrase-based statistical machine translation (SMT) models in several languages (Luong et al., 2015a; Eriguchi et al., 2016). Those NMT models are ba- 175 Proceedings of the 3rd Workshop on Asian Translation, pages 175–183, 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
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
Page 155 and 156: #$ #%$ #$ !""
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
Page 183 and 184: proportionally slower to compute (a
Page 185 and 186: 3.6 Ensembling Ensembling has previ
Page 187: when Chinese is also the language m
Page 191 and 192: where W s ∈ R d×d is a matrix an
Page 193 and 194: Model BLEU (BP) RIBES Character-bas
Page 195 and 196: The word-based NMT models cannot us
Page 197 and 198: Razvan Pascanu, Tomas Mikolov, and
Page 199 and 200: governs the grammaticality of the t
Page 201 and 202: 2.1 Quantization and Binarization o
Page 203 and 204: 3 Results Team Other Resources Syst
Page 205 and 206: Peter F Brown, John Cocke, Stephen
Page 207 and 208: Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210: Language Sentence Chinese 该 / 钽
Page 211 and 212: Chinese or Japanese sentences Chine
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
Page 219 and 220: Figure 2: Flow of the voice predict
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Page 223 and 224: controlling the sentence length wit
Page 225 and 226: Chinese-to-Japanese Patent Machine
Page 227 and 228: using the reordering oracles over t
Page 229 and 230: Conference on Natural Language Proc
Page 231 and 232: Figure 1: Hierarchical approach wit
Page 233 and 234: newswire test and development set o
Page 235 and 236: Source: the rain and cold wind on W
Page 237 and 238: 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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