December 11-16 2016 Osaka Japan

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Subtask Ja → En En → Ja Ja → Zh Zh → Ja Model EBMT NMT EBMT NMT EBMT NMT EBMT NMT Human Evaluation 1 2 1 1 1 2 BLEU 21.22 24.71 26.22 31.03 36.19 30.27 31.98 36.63 46.04 44.29 RIBES 70.57 75.08 75.66 77.12 81.98 81.31 83.76 82.03 87.65 86.94 AM-FM 59.52 56.27 55.85 74.75 73.87 76.42 76.33 76.71 78.59 78.44 Pairwise - 47.00 44.25 - 55.25 30.75 58.75 - 63.75 56.00 Rank - 3/9 4/9 - 1/10 3/5 1/5 - 1/9 2/9 JPO adequacy - 3.89 - - 4.02 - 3.88 - 3.94 - Rank - 1/3 - - 1/4 - 1/3 - 1/3 - Table 1: Official automatic and human evaluation results of our EBMT and NMT systems for the ASPEC subtasks. Source Reference EBMT NMT Shown here are type and basic configuration and standards of this flow with some diagrams. This flow sensor type and the basic composition, standard is illustrated, and introduced. This paper introduces the type, basic configuration, and standards of this flow sensor. Table 2: A Japanese-to-English translation example by our EBMT and NMT systems. 4.2 Official Evaluation Results Table 1 shows the official automatic and human evaluation results of the ASPEC subtasks that we participated in. “Rank” shows the ranking of our submissions among all the submissions for each subtask. In view of the pairwise evaluation, our system achieved the best translation quality for all the subtasks except for Ja → En. The difference of the pairwise scores between the best system and our system for Ja → En is 1.25, which is not statistically significant. For all the other subtasks, the differences between our system and others are statistically significant (p < 0.01). As for the JPO adequacy evaluation, our system achieved the best translation quality for all the subtasks. The differences of the score between our system and the 2nd graded systems are 0.058, 0.305, 0.245 and 0.300 for Ja → En, En → Ja, Ja → Zh and Zh → Ja respectively. 4.3 Error Analysis We analyzed the translation results of both our EBMT and NMT systems. We found that the biggest problem of EBMT is word order errors, which affects the fluency and meaning of the translations. This is due to the reason that the word order of the translations in EBMT depends on the parse trees of the input sentences, but the parsing accuracy is not perfect especially for Chinese. NMT tends to produce fluent translations, however it lacks of adequacy sometimes. The most common problem of NMT is that it could produce under or over translated translations, due to the lack of a way for the attention mechanism to memorize the source words that have been translated during decoding. We plan to address this problem with the coverage model proposed in (Tu et al., 2016). The UNK words are also a big problem. Although we deal with them using the UNK replacement method (Luong et al., 2015), it could simply fail because of errors for finding the corresponding source words using attention. We show a Japanese-to-English translation example by our EBMT and NMT systems in Table 2 to illustrate some of the above problems. The translation produced by the EBMT system has a word order problem that changes the meaning, making “the basic composition, standard” independent from “this flow sensor.” It also has an agreement violation problem between the argument and the predicate that “is illustrated” should be “are illustrated”. The translation produced by the NMT system is more fluent, but it does not translate the source word “ (be illustrated)”. In addition, it adds the additional information “this paper” to the translation. It is interesting to note that the performance gap between NMT and EBMT is largest for Zh → Ja, 172
when Chinese is also the language most difficult to parse. The gap is smaller when Japanese, the easiest language to parse, is the source language. We can infer that the EBMT system is somehow quite handicapped by source sentence parsing issues, as we had noted in our previous results. 5 Conclusion We have described our experiments for WAT 2016 using both an EBMT system and a NMT system. We could obtain the best or close-to-best results in each translation task. The NMT approach proved to be quite successful, which is in line with other recent MT evaluation results (Bojar et al., 2016). In the future, we will probably continue to explore the NMT approach, if possible merging in some elements of our EBMT system. We could then hope to solve the weaknesses of the two systems that we identified in our error analysis. References Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR 2015. Ondrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, et al. 2016. Findings of the 2016 conference on machine translation (wmt16). In Proceedings of WMT 2016. Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. Fabien Cromieres. 2016. Kyoto-NMT: a neural machine translation implementation in Chainer. In Coling 2016 System Demonstration. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780. Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Minh-Thang Luong, Ilya Sutskever, Quoc V Le, Oriol Vinyals, and Wojciech Zaremba. 2015. Addressing the rare word problem in neural machine translation. In Proceedings of ACL 2015. Thang Luong, Kyunghyun Cho, and Christopher D. Manning. 2016. Neural machine translation (ACL tutorial). https://sites.google.com/site/acl16nmt/. Toshiaki Nakazawa, Hideya Mino, Chenchen Ding, Isao Goto, Graham Neubig, Sadao Kurohashi, and Eiichiro Sumita. 2016a. Overview of the 3rd workshop on asian translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), Osaka, Japan, October. Toshiaki Nakazawa, Manabu Yaguchi, Kiyotaka Uchimoto, Masao Utiyama, Eiichiro Sumita, Sadao Kurohashi, and Hitoshi Isahara. 2016b. Aspec: Asian scientific paper excerpt corpus. In Proceedings of the 10th Conference on International Language Resources and Evaluation (LREC2016), Portoroz, Slovenia, 5. John Richardson, Raj Dabre, Chenhui Chu, Fabien Cromières, Toshiaki Nakazawa, and Sadao Kurohashi. 2015. KyotoEBMT System Description for the 2nd Workshop on Asian Translation. In Proceedings of the 2nd Workshop on Asian Translation (WAT2015), pages 54–60, Kyoto, Japan, October. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Edinburgh neural machine translation systems for wmt 16. In Proceedings of the first Conference on Machine Translation (WMT2016). Mo Shen, Li Wingmui, HyunJeong Choe, Chenhui Chu, Daisuke Kawahara, and Sadao Kurohashi. 2016. Consistent word segmentation, part-of-speech tagging and dependency labelling annotation for chinese language. In Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan, December. Association for Computational Linguistics. Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929– 1958. 173
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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
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
Page 183 and 184: proportionally slower to compute (a
Page 185: 3.6 Ensembling Ensembling has previ
Page 189 and 190: Character-based Decoding in Tree-to
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
Page 221 and 222: ecause the number of passive senten
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
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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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