December 11-16 2016 Osaka Japan

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Hideki Mima and Sophia Ananiadou. 2001. An application and evaluation of the C/NC-value approach for the automatic term recognition of multi-word units in Japanese. Terminology, 6(2):175–194. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In ACL 2002, pages 311–318. 202
Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, and Mamoru Komachi Tokyo Metropolitan University {yamagishi-hayahide, kanouchi-shin, sato-takayuki} at ed.tmu.ac.jp, komachi at tmu.ac.jp Abstract In machine translation, we must consider the difference in expression between languages. For example, the active/passive voice may change in Japanese-English translation. The same verb in Japanese may be translated into different voices at each translation because the voice of a generated sentence cannot be determined using only the information of the Japanese sentence. Machine translation systems should consider the information structure to improve the coherence of the output by using several topicalization techniques such as passivization. Therefore, this paper reports on our attempt to control the voice of the sentence generated by an encoder-decoder model. To control the voice of the generated sentence, we added the voice information of the target sentence to the source sentence during the training. We then generated sentences with a specified voice by appending the voice information to the source sentence. We observed experimentally whether the voice could be controlled. The results showed that, we could control the voice of the generated sentence with 85.0% accuracy on average. In the evaluation of Japanese-English translation, we obtained a 0.73-point improvement in BLEU score by using gold voice labels. 1 Introduction In a distant language pair such as Japanese-English, verbs between the source language and the target language are often used differently. In particular, the voices of the source and target sentences are sometimes different in a fluent translation when considering the discourse structure of the target side because Japanese is a pro-drop language and does not the use passive voice for object topicalization. In Table 1, we show the number of occurrences of each voice in high-frequency verbs in Asian Scientific Paper Expert Corpus (ASPEC; Nakazawa et al. (2016b)). In the top seven high frequency verbs, “show” tended to be used in active voice, whereas “examine,” “find,” and “observe” tended to be used in the passive voice. However, “describe,” “explain,” and “introduce” tended not to be used in any particular voice. For example, the voice of the verb “introduce” could not be determined uniquely, because it was sometimes used in phrases like “This paper introduces ...” and, sometimes, “... are introduced.” Therefore, it is possible that the translation model failed to learn the correspondence between Japanese and English. Recently, recurrent neural networks (RNNs) such as encoder-decoder models have gained considerable attention in machine translation because of their ability to generate fluent sentences. However, compared to traditional statistical machine translation, it is not straightforward to interpret and control the output of the encoder-decoder models. Several attempts have been made to control the output of the encoderdecoder models. First, Kikuchi et al. (2016) proposed a new Long Short-Term Memory (LSTM) network to control the length of the sentence generated by an encoder-decoder model in a text summarization task. In their experiment, they controlled the sentence length while maintaining the performance compared to the results of previous works. Second, Sennrich et al. (2016) attempted to control the honorific in English-German neural machine translation (NMT). They trained an attentional encoder-decoder model using English (source) data to which the honorific information of a German (target) sentence was added. They restricted the honorific on the German side at the test phase. 203 Proceedings of the 3rd Workshop on Asian Translation, pages 203–210, 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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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
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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
Page 195 and 196: The word-based NMT models cannot us
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Page 201 and 202: 2.1 Quantization and Binarization o
Page 203 and 204: 3 Results Team Other Resources Syst
Page 205 and 206: Peter F Brown, John Cocke, Stephen
Page 207 and 208: Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210: Language Sentence Chinese 该 / 钽
Page 211 and 212: Chinese or Japanese sentences Chine
Page 213 and 214: 4 Experiments and Results 4.1 Chine
Page 215: 6 Conclusion and Future Work In thi
Page 219 and 220: Figure 2: Flow of the voice predict
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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
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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December 11-16 2016 Osaka Japan

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