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Input Oracle Segments Oracle Translation Predicted Segments Machine Translation i myself think that the argument about smoking is a slightly misleading one but it ’s not predicted to go as high as once believed we figured the ultimate test would be to ask the dog ’s owner to leave i myself think that the argument about smoking is a slightly misleading one but it ’s not predicted to go as high as once believed we figured the ultimate test would be to ask the dog ’s owner to leave 私自身が考えるのは喫煙についての議論は少し誤解を招くものだということですしかし予測できないのはその高さがかつて思われていたのと同じ位になるということです我々が考えたのは最終的なテストは犬の所有者に退去するよう依頼することです i myself think that the argument about smoking is a slightly misleading one but it ’s not predicted to go as high as once believed we figured the ultimate test would be to ask the dog ’s owner to leave 私は自分の喫煙に関する議論が少し誤解を招くことと思いますしかし信じられるように高くなることが予測されていません私たちが予想していたたら最終的なテストは犬の所有者に出発するのだと考えられていません Table 4: Example of Threshold-Latency-based Sentence Segmentor. “the ultimate test would be” occurs as a sentence beginning in the training corpus of the language model. A language model can only capture local patterns, and cannot understand structures of compound sentences. This is a weakness of applying n-gram language modeling techniques to sentence segmentation. As a solution, it may be advantageous to replace the n-gram models with recurrent neural networks that are strong at exploiting long-distant context, and we plan to explore this in the future. It is interesting to note that, the resulting translations of the wrong segmentation “we figured” and “the ultimate test would be . . . ” are decent, as the origin meaning is delivered. This was an unexpected bonus that we owe to the evaluation framework. The evaluation framework in this paper is end-to-end BLEU, and places no constraints on segmentation positions. This helped to tune the parameters of the proposed methods properly. To sum up, this example illustrates that the proposed methods work reasonably well, and the evaluation framework itself is also making a contributions. However, errors caused by lack of understanding whole sentence structure are inevitable, and these need to be addressed in future work. 5 Conclusion This paper proposed and studied a segmentation boundary confidence score and a set of online segmentation strategies for simultaneous interpretation. The solution expressed by Equations 4 and 7 was proven empirically to be both effective and efficient. The choice to use sentence segmentation units was motivated by the desire to handle difficult language pairs that require long-distance intra-sentential word re-ordering (for example the Japanese-English pair studied in this paper). For these cases, using smaller units than sentences will prevent the translation system from being able to correctly re-order the words. For easier language pairs, segments shorter than sentences may be preferable; note that the proposed confidence score can be easily modified to handle sub-sentential segments if necessary. We would like to study this in the context of other language pairs in the future. The primary motivation for this work was to create an online version of the hidden n-gram approach (Stolcke and Shriberg, 1996; Stolcke et al., 1998), a de facto standard method that is often used for sentence segmentation due to its effectiveness, simplicity and speed. However, it has a latency issue that prevents it from being used in simultaneous interpretation. The proposed method alleviates this latency issue while preserving all its merits, and we show empirically that the new method maintains the effectiveness of the hidden n-gram method even when the future context is reduced as far as a single word. We believe that the proposed method will not only lead to workable systems, but also establish 146
a meaningful baseline for related research. In the long term, we plan to incorporate the findings in this paper into an industrial simultaneous interpretation system. References Anonymous. 2016. A study of punctuation handling for speech-to-speech translation. In 22nd Annual Meeting on Natural Language Processing, page to appear. Srinivas Bangalore, Vivek Kumar Rangarajan Sridhar, Prakash Kolan, Ladan Golipour, and Aura Jimenez. 2012. Real-time incremental speech-to-speech translation of dialogs. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 437–445. Association for Computational Linguistics. Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning, 20(3):273–297. Andrew Finch, Xiaolin Wang, and Eiichiro Sumita. 2014. An Exploration of Segmentation Strategies in Stream Decoding. In IWSLT. Christian Fügen, Alex Waibel, and Muntsin Kolss. 2007. Simultaneous translation of lectures and speeches. Machine Translation, 21(4):209–252. Tomoki Fujita, Graham Neubig, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. 2013. Simple, lexicalized choice of translation timing for simultaneous speech translation. In INTERSPEECH, pages 3487–3491. Isao Goto, Ka Po Chow, Bin Lu, Eiichiro Sumita, and Benjamin K Tsou. 2013. Overview of the patent machine translation task at the ntcir-10 workshop. In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering and Cross-Lingual Information Access, NTCIR-10. Thanh-Le Ha, Jan Niehues, Eunah Cho, Mohammed Mediani, and Alex Waibel. 2015. The KIT Translation Systems for IWSLT 2015. In Proceedings of the twelveth International Workshop on Spoken Language Translation (IWSLT), Da Nang, Veitnam, pages 62–69. Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of MT Summit, volume 5, pages 79–86. Taku Kudo. 2005. Mecab: Yet another part-of-speech and morphological analyzer. http://mecab. sourceforge. net/. Michaeel Kzai, Brian Thompson, Elizabeth Salesky, Timothy Anderson, Grant Erdmann, Eric Hansen, Brian Ore, Katherine Young, Jeremy Gwinnup, Michael Hutt, and Christina May. 2015. The MITLL-AFRL IWSLT 2015 Systems. In Proceedings of the twelveth International Workshop on Spoken Language Translation (IWSLT), Da Nang, Veitnam, pages 23–30. John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the eighteenth international conference on machine learning, ICML, volume 1, pages 282–289. Wei Lu and Hwee Tou Ng. 2010. Better punctuation prediction with dynamic conditional random fields. In Proceedings of the 2010 conference on empirical methods in natural language processing, pages 177–186. Association for Computational Linguistics. Evgeny Matusov, Gregor Leusch, Oliver Bender, Hermann Ney, et al. 2005. Evaluating machine translation output with automatic sentence segmentation. In IWSLT, pages 138–144. Citeseer. Evgeny Matusov, Arne Mauser, and Hermann Ney. 2006. Automatic sentence segmentation and punctuation prediction for spoken language translation. In IWSLT, pages 158–165. Yusuke Oda, Graham Neubig, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. 2014. Optimizing segmentation strategies for simultaneous speech translation. In ACL (2), pages 551–556. 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. 147
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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
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 and 122: For long sentence, we discarded all
Page 123 and 124: As shown in the above tables and fi
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: Sentence Segmenter Parameters Dev.
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 197 and 198: Razvan Pascanu, Tomas Mikolov, and
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Page 201 and 202: 2.1 Quantization and Binarization o
Page 203 and 204: 3 Results Team Other Resources Syst
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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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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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