December 11-16 2016 Osaka Japan

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Michael Collins, Philipp Koehn, and Ivona Kučerová. 2005. Clause restructuring for statistical machine translation. In Proceedings of the 43rd annual meeting on association for computational linguistics, pages 531–540. Association for Computational Linguistics. Kenneth Heafield. 2011. Kenlm: Faster and smaller language model queries. In Proceedings of the Sixth Workshop on Statistical Machine Translation, pages 187–197. Association for Computational Linguistics. Reinhard Kneser and Hermann Ney. 1995. Improved backing-off for m-gram language modeling. In Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on, volume 1, pages 181–184. IEEE. Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, pages 48–54. Association for Computational Linguistics. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 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, pages 177–180. Association for Computational Linguistics. Toshiaki Nakazawa, Hideya Mino, Chenchen Ding, Isao Goto, Graham Neubig, Sadao Kurohashi, and Eiichiro Sumita. 2016. Overview of the 3rd workshop on asian translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), Osaka, Japan, December. Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational linguistics, 29(1):19–51. Franz Josef Och. 2003. Minimum error rate training in statistical machine translation. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, pages 160–167. Association for Computational Linguistics. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics, pages 311–318. Association for Computational Linguistics. Raj Nath Patel, Rohit Gupta, Prakash B Pimpale, and Sasikumar M. 2013. Reordering rules for englishhindi smt. In Proceedings of the Second Workshop on Hybrid Approaches to Translation, pages 34–41. Association for Computational Linguistics. Ananthakrishnan Ramanathan, Jayprasad Hegde, Ritesh M Shah, Pushpak Bhattacharyya, and M Sasikumar. 2008. Simple syntactic and morphological processing can help english-hindi statistical machine translation. In IJCNLP, pages 513–520. David Vilar, Jia Xu, Luis Fernando d’Haro, and Hermann Ney. 2006. Error analysis of statistical machine translation output. In Proceedings of LREC, pages 697–702. 222
Residual Stacking of RNNs for Neural Machine Translation Raphael Shu The University of Tokyo shu@nlab.ci.i.u-tokyo.ac.jp Akiva Miura Nara Institute of Science and Technology miura.akiba.lr9@is.naist.jp Abstract To enhance Neural Machine Translation models, several obvious ways such as enlarging the hidden size of recurrent layers and stacking multiple layers of RNN can be considered. Surprisingly, we observe that using naively stacked RNNs in the decoder slows down the training and leads to degradation in performance. In this paper, We demonstrate that applying residual connections in the depth of stacked RNNs can help the optimization, which is referred to as residual stacking. In empirical evaluation, residual stacking of decoder RNNs gives superior results compared to other methods of enhancing the model with a fixed parameter budget. Our submitted systems in WAT2016 are based on a NMT model ensemble with residual stacking in the decoder. To further improve the performance, we also attempt various methods of system combination in our experiments. 1 Introduction Recently, the performance of machine translation is greatly improved by applying neural networks partially in a Statistical Machine Translation (SMT) pipeline (Zou et al., 2013) or training a end-to-end neural network based machine translation model (Sutskever et al., 2014). The latter approach, or Neural Machine Translation (NMT), possess several advantages compared to conventional SMT approaches. Firstly, as the translation process is done with a single network, the pipeline of NMT training is simpler. This advantage also indicates a much lower implementation cost. Developing a SMT decoder will cost one to three months for a graduate student, while training a NMT model requires merely a script that contains the network definition. Secondly, the memory consumption is vastly reduced when translating with a NMT model. While a conventional phrase-based model trained on a large bilingual corpus can easily consumes over 100GB memory space, a trained NMT model typically requires less than 1GB GPU on-board memory in test time. The most significant difference of NMT approach in contrast with SMT is that the information for producing the translation is held in continuous space but not discrete values. While this characteristics of neural network introduces huge difficulties in debugging, this allows the model to learn translation knowledge beyond the conventional hand-crafted MT framework. Recently, even the word embeddings obtained from NMT training demonstrates advantages over specific tasks (Hill et al., 2014). The network architectures of NMT models are simple but effective. It produces a sentence representation with the encoder, then the decoder generates the translation from the vector of sentence representation. Generally the encoder is composed of a single recurrent neural network (RNN). The decoder reads the vector representations created by the encoder and produce a series of output vectors. A linear transformation is applied to each vector in order to create a large softmax layer in size of the whole vocabulary. Soft attention mechanism introduced in (Bahdanau et al., 2014) further boosted the performance by increasing the computational complexity. With the advance of deep learning, deeper network architectures are favored as the models become more expressive with more layers. An absolute way to deepening NMT models is to stack multiple layers of RNN in either encoder or decoder side. However, our experiments show that stacking RNNs naively in the decoder will cause significant slowdown in training and results in degraded performance. 223 Proceedings of the 3rd Workshop on Asian Translation, pages 223–229, 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
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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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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 239 and 240: 2.2 Generalized Minimum Bayes Risk
Page 241 and 242: 5.0 4.5 4.0 Baseline model Enlarged
Page 243: Felix Hill, Kyunghyun Cho, Sebastie
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December 11-16 2016 Osaka Japan

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