December 11-16 2016 Osaka Japan

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Model RIBES(%) BLEU(%) HUMAN Online A 71.52 19.81 49.750 T2S system 1-best 77.87 32.32 - T2S neural reranking 78.29 33.57 - Ensemble of 2 NMT models with residual decoder RNNs (submission 1) 81.72 33.38 30.500 + GMBR system combination with T2S results 80.90 34.25 - + System combination with a simple heuristics (submission 2) 81.44 34.77 29.750 Table 2: Official evaluation results of WAT2016 6 Related Works Deep residual learning proposed in (He et al., 2015) learns a residual representation with a deep neural network. As stacking new layers does not lengthen the backprop path of early layers, residual learning enables the training of very deep networks, such as those with 1000 layers. Deep residual nets won the 1st place in ILSVRC 2015 classification task. The success of deep residual learning gives the insight of a better deep architecture of neural nets. Beyond the success of residual learning, applying this technique to recurrent nets is a promising direction, which is researched in several previous works. Recurrent Highway Networks (Srivastava et al., 2015) enhance the LSTM by adding an extra residual computation in each step. The experiments show the Recurrent Highway Networks can achieve better perplexity in language modeling task with a limited parameter budget. (Liao and Poggio, 2016) achieved similar classification performance when using shared weights in a ResNet, which is exactly a RNN. Pixel Recurrent Neural Networks (van den Oord et al., 2016) demonstrates a novel architecture of neural nets with two-dimensional recurrent neural nets using residual connections. Their models achieved better log-likelihood on image generation tasks. Remarkably, the neural network architecture described in a lecture report 3 is similar to our models in spirit, where they applied stochastic residual learning to both depth and horizontal timesteps, which leads to better classification accuracy in Stanford Sentiment Treebank dataset. 7 Conclusion In this paper, we demonstrates the effects of several possible approaches of enhancing the decoder RNN in NMT models. Surprisingly, Stacking multi-layer LSTMs in the decoder hinders the training and results in low performance in our experiments. Through empirical evaluation of several decoder architectures, we show that applying residual connections in the deep recurrent nets leads to superior results with same parameters as Stacked LSTMs. The advantage of the using of residual RNN in the decoder provides insights on the correct ways of enhancing NMT models. 8 Acknowledgment We thank Weblio Inc. for financial support of this research. References Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. Kevin Duh, Katsuhito Sudoh, Xianchao Wu, Hajime Tsukada, and Masaaki Nagata. 2011. Generalized minimum bayes risk system combination. In IJCNLP, pages 1356–1360. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385. 3 https://cs224d.stanford.edu/reports/PradhanLongpre.pdf 228
Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, and Yoshua Bengio. 2014. Embedding word similarity with neural machine translation. arXiv preprint arXiv:1412.6448. Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010. Automatic evaluation of translation quality for distant language pairs. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 944–952. Association for Computational Linguistics. Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Qianli Liao and Tomaso Poggio. 2016. Bridging the gaps between residual learning, recurrent neural networks and visual cortex. arXiv preprint arXiv:1604.03640. Thang Luong, Hieu Pham, and D. Christopher Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412–1421. Association for Computational Linguistics. 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, December. 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 Ninth International Conference on Language Resources and Evaluation (LREC 2016), pages 2204–2208, Portoro, Slovenia, may. European Language Resources Association (ELRA). Graham Neubig. 2013. Travatar: A forest-to-string machine translation engine based on tree transducers. In Proceedings of the ACL Demonstration Track, Sofia, Bulgaria, August. Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2015. Minimum risk training for neural machine translation. arXiv preprint arXiv:1512.02433. Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015. Highway networks. arXiv preprint arXiv:1505.00387. Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104–3112. Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. In ICML. Will Y Zou, Richard Socher, Daniel M Cer, and Christopher D Manning. 2013. Bilingual word embeddings for phrase-based machine translation. In EMNLP, pages 1393–1398. 229
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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
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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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Page 207 and 208: Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210: Language Sentence Chinese 该 / 钽
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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
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Page 225 and 226: Chinese-to-Japanese Patent Machine
Page 227 and 228: using the reordering oracles over t
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Page 231 and 232: Figure 1: Hierarchical approach wit
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Page 235 and 236: Source: the rain and cold wind on W
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Page 241: 5.0 4.5 4.0 Baseline model Enlarged
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December 11-16 2016 Osaka Japan

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