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Team System BLEU HUMAN SENSE Vanilla PBMT (clustercat) 25.31 1.250 SENSE Vanilla PBMT (mkcls) 25.16 -2.750 ORGRANIZER Online A 24.20 35.75 ORGRANIZER Baseline PBMT 23.95 0 IITB Bilingual Neural LM 22.35 -9.250 ORGRANIZER Online B 18.09 10.50 Table 3: Results of WAT2016 English-Indonesian News Domain Task Team System BLEU HUMAN ORGANIZER Online A 28.11 49.25 SENSE Vanilla PBMT (clustercat) 25.97 -8.25 SENSE Vanilla PBMT (mkcls) 25.62 -5.00 ORGANIZER Baseline PBMT 24.57 0 IITB Bilingual Neural LM 22.58 - ORGANIZER Online B 19.69 34.50 Table 4: Results of WAT2016 Indonesian-English News Domain Task Table 3 and 4 presents the results for the Indonesian-English News Domain Task. From Table 3, we achieve the highest BLEU scores in the English-Indonesia direction with a difference of >1.0+ BLEU score with respect to the baseline PBMT provided by the organizers. However, our HUMAN scores show that the quality of our system output is only marginally better than the baseline. Comparatively, the online system A has similar BLEU scores to the organizer’s baseline but achieved stellar HUMAN scores of +35.75. Table 4 shows the results for the English-Indonesian task, the online system A and B achieved the best HUMAN scores. In both directions, we see the same automatic vs manual evaluation disparity from System B’s low BLEU and high HUMAN scores and from our system’s high BLEU and low/marginal HUMAN scores. 4 Conclusion We motivate and describe the steps to build a fast and light phrase-based machine translation model that achieved comparable results to the WAT2016 baseline. We hope that our baseline system helps new MT practitioners that are not familiar with the Moses ecology 13 to build PBMT models. The full training script is available on https://github.com/alvations/vanilla-moses/blob/ master/train-vanilla-model.sh. Acknowledgements The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement no. 317471. References Yaser Al-Onaizan and Kishore Papineni. 2006. Distortion models for statistical machine translation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pages 529–536. Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016. Neural versus Phrase-Based Machine Translation Quality: a Case Study. ArXiv e-prints, August. Alexandra Birch, Miles Osborne, and Phil Blunsom. 2010. Metrics for mt evaluation: evaluating reordering. Machine Translation, 24(1):15–26. 13 Our Vanilla PBMT system is complimentary to the steps described in http://www.statmt.org/moses/?n=Moses.Tutorial 190
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In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 263–270. Association for Computational Linguistics. Josep Crego, Jungi Kim, Guillaume Klein, Anabel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, Raoum Khiari, Byeongil Ko, Catherine Kobus, Jean Lorieux, Leidiana Martins, Dang-Chuan Nguyen, Alexandra Priori, Thomas Riccardi, Natalia Segal, Christophe Servan, Cyril Tiquet, Bo Wang, Jin Yang, Dakun Zhang, Jing Zhou, and Peter Zoldan. 2016. SYSTRAN’s Pure Neural Machine Translation Systems. ArXiv e-prints, October. Jon Dehdari, Liling Tan, and Josef van Genabith. 2016a. Bira: Improved predictive exchange word clustering. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1169–1174, San Diego, California, June. 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In Software Engineering, Testing, and Quality Assurance for Natural Language Processing, pages 49–57. Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. 2013. Scalable modified Kneser-Ney language model estimation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 690–696, Sofia, Bulgaria. Kenneth Heafield. 2011. KenLM: Faster and smaller language model queries. In Proceedings of the EMNLP 2011 Sixth Workshop on Statistical Machine Translation, pages 187–197, Edinburgh, Scotland, United Kingdom. Hieu Hoang and Adam Lopez. 2009. A unified framework for phrase-based, hierarchical, and syntax-based statistical machine translation. In In Proceedings of the International Workshop on Spoken Language Translation (IWSLT, pages 152–159. Hieu Hoang. 2011. Improving statistical machine translation with linguistic information. The University of Edinburgh. Marcin Junczys-Dowmunt. 2012. Phrasal rank-encoding: Exploiting phrase redundancy and translational relations for phrase table compression. The Prague Bulletin of Mathematical Linguistics, 98:63–74. 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. Kevin Knight. 1999. Decoding complexity in word-replacement translation models. Computational Linguistics, 25(4):607– 615. 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. 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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
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 and 186: 3.6 Ensembling Ensembling has previ
Page 187 and 188: when Chinese is also the language m
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
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Page 201 and 202: 2.1 Quantization and Binarization o
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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 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
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Page 235 and 236: Source: the rain and cold wind on W
Page 237 and 238: Residual Stacking of RNNs for Neura
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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