December 11-16 2016 Osaka Japan

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RIBES BLEU Task System BLEU RIBES HUMAN JPCenja JPCjaen JPCzhja JPCjazh PB SMT 34.26 0.728 -- HPB SMT 36.61 0.779 -- T2S SMT 37.65 0.797 -- GRUSearch 40.00 0.833 -- GRUSearch+UNKreplace 41.16 0.840 39.50 PB SMT 30.80 0.730 -- HPB SMT 32.23 0.763 -- T2S SMT 34.40 0.793 -- GRUSearch 38.13 0.836 -- GRUSearch+UNKreplace 41.62 0.852 41.63 PB SMT 38.51 0.779 -- HPB SMT 39.52 0.802 -- T2S SMT 39.45 0.810 -- GRUSearch 38.24 0.820 -- GRUSearch+UNKreplace 39.72 0.831 32.25 PB SMT 30.60 0.787 -- HPB SMT 30.26 0.788 -- T2S SMT 31.05 0.794 -- GRUSearch 31.03 0.819 -- GRUSearch+UNKreplace 31.49 0.823 -1.00 Table 3: Official automatic evaluation results on JPC We also demonstrate the comparison results on BLEU and on RIBES in Figure 3 and Figure 4, separately. 45 35 25 15 en-ja ja-en zh-ja ja-zh JPCen-ja JPCja-en JPCzh-ja JPCja-zh Tasks PB SMT HP SMT T2S SMT GRUSearch GRUSearch+UNKreplace Figure 3: BLEU scores of all systems 0.9 0.85 0.8 0.75 0.7 0.65 0.6 en-ja ja-en zh-ja ja-zh JPCen-ja JPCja-en JPCzh-ja JPCja-zh Tasks PB SMT HP SMT T2S SMT GRUSearch GRUSearch+UNKreplace Figure 4: RIBES scores of all systems 108
As shown in the above tables and figures, our systems, both the GRUSearch+UNKreplace and GRU- Search outperformed the baseline systems in most tasks. In addition, our system with unknown word solution, GRUSearch+UNKreplace performed much better than the system without the unknown word solution, GRUSearch. It is proved that our unknown word translation approach is effective. Therefore, we submitted GRUSearch+UNKreplace to WAT2016 for human evaluation. And all the Pairwise scores of our tasks except JPCja-zh are much bigger than zero, which further proved that GRUSearch+UN- Kreplace performed better than baseline system. Specifically, in the JPCja-en task, GRUSearch+UNKreplace achieved an improvement of 7.22 of BLEU score, compared with T2S SMT. GRUSearch+UNKreplace also achieved an improvement of 3.49 of BLEU, compared with GRUSearch. It means that the effectiveness of our unknown word resolution achieved good performance by the support of a better attention network, and a better dictionary, which obtained from higher quality of training data. However, our model shows great difference in different tasks, in two tasks, our system performs even worse than the baseline systems. It is considered that we need do more works to find the best hyperparameters of these tasks. The hyper-parameter optimization will be one of the most important tasks of our future work. 4 Conclusion In this paper, we described our NMT system which used RNN and GRU, and we adopt the attention mechanism into the encoder–decoder network. We also presented a translation-specific approach to solve the unknown words translation problem. Experiment results show that our system performs good performance in most of the evaluation tasks. However, there exists some space to improve the performance of our system: The solution for dealing with unknown words is still an open question; Hyper-parameter optimization is one of the most important tasks in NMT system. We also will try to integrate morphological features such as part-ofspeech tags, syntactic dependency labels as input features into NMT systems, to improve model quality, aiming at further improvement of translation results. Reference Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. Computer Science. Cheng, Yong, et al. 2015. Agreement-based joint training for bidirectional attention-based neural machine translation. arXiv preprint arXiv:1512.04650. Cho, Kyunghyun, et al. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.Firat, Orhan, Kyunghyun Cho, and Yoshua Bengio. 2016. Multiway, multilingual neural machine translation with a shared attention mechanism. arXiv preprint arXiv:1601.01073. Cho, Kyunghyun, et al. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259. Firat, Orhan, Kyunghyun Cho, and Yoshua Bengio. "Multi-way, multilingual neural machine translation with a shared attention mechanism." arXiv preprint arXiv:1601.01073 (2016). Forcada, Mikel L., and Ramón P. Ñeco. 1997. Recursive hetero-associative memories for translation. In International Work-Conference on Artificial Neural Networks (pp. 453-462). Springer Berlin Heidelberg. Kalchbrenner, Nal, and Phil Blunsom. 2013. Recurrent Continuous Translation Models. In EMNLP (Vol. 3, No. 39, p. 413). Nakazawa, Toshiaki, et al. 2016. Overview of the 3rd Workshop on Asian Translation. Proceedings of the 3rd Workshop on Asian Translation (WAT2016). Schuster, Mike, and Kuldip K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. Sennrich, Rico, and Barry Haddow. 2016. Linguistic Input Features Improve Neural Machine Translation. arXiv preprint arXiv:1606.02892. 109
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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
Page 71 and 72: K. Papineni, S. Roukos, T. Ward, an
Page 73 and 74: Table 1: Examples of title, ingredi
Page 75 and 76: text. In this section, we explain t
Page 77 and 78: Table 3: Automatic evaluation BLEU/
Page 79 and 80: 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
Page 85 and 86: dimensions of 150 units in second h
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: For long sentence, we discarded all
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 and 132: former system can translate more th
Page 133 and 134: 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
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
Page 167 and 168: -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3
Page 169 and 170: 1.800 1.600 1.400 1.200 1.000 0.800
Page 171 and 172: 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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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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