December 11-16 2016 Osaka Japan

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# Active # Passive # Error Accuracy BLEU Reference 100 100 0 — — Baseline (No labels) 74 117 9 (72.0) 20.53 ALL_ACTIVE 151 36 13 75.5 19.93 ALL_PASSIVE 17 175 8 87.5 19.63 REFERENCE 97 94 9 89.5 21.26 PREDICT 72 121 7 87.5 20.42 Table 2: Accuracy of voice controlling and BLEU score of the translation. the extent to which the voice of the generated sentence was controlled correctly. ALL_ACTIVE. Controlling all target sentences to the active voice. ALL_PASSIVE. Controlling all target sentences to the passive voice. REFERENCE. Controlling each target sentence to the same voice as that of the reference sentence. PREDICT. Controlling each target sentence to the predicted voice. There were two reasons for testing ALL_ACTIVE and ALL_PASSIVE: to evaluate how correctly we counld control the voice, and to discuss the source of errors. In REFERENCE, the generated sentences tended to be natural. However, in ALL_ACTIVE and ALL_PASSIVE, the generated sentences were sometimes unnatural in terms of the voice. We identified these sentences to investigate the reasons why these errors occurred. We checked the voice of the generated sentence and calculated the accuracy manually because the performance of voice labeling depends on the performance of the parser. We used the Stanford Parser (ver. 3.5.2) to parse the English sentence. The labelling performance was 95% in this experiment. We used CaboCha (ver. 0.68; Kudo and Matsumoto (2002)) to obtain the root phrase of the Japanese sentence in PREDICT. If the sentence was a complex sentence, we checked the voice of the root verb 2 . The test data of ASPEC consisted of 1,812 sentences in total. The evaluation data for the voice controlling consisted of 100 passive sentences and 100 active sentences chosen from the top of the test data. We did not consider subject and object alternation because this evaluation only focused on the voice of the sentence. Only one evaluator performed an annotation. In this experiment, the accuracy was calculated as the agreement between the label and the voice of the generated sentence. “Error sentence” means the root verb of the generated sentence could not be distinguished manually, or it did not include a verb, and so on. The baseline was an attentional encoder-decoder by Bahdanau et al. (2015), which does not control the voice. In the evaluation of the Japanese-English translation, we calculated the BLEU (Papineni et al., 2002) score with the test data of all 1,812 sentences. At the training phase, we used 827,503 sentences, obtained by eliminating sentences with more than 40 words in the first 1 million sentences of the ASPEC. Word2Vec 3 (Mikolov et al., 2013) was trained with all 3 million sentences of ASPEC. The vocabulary size was 30,000 4 . The dimension of the embeddings and hidden units was 512. The batch size was 128. The optimizer was Adagrad, and the learning rate was 0.01. We used Chainer 1.12 (Tokui et al., 2015) to implementing the neural network. 4 Result and Discussion 4.1 Experiments with a Gold Voice Label Table 2 shows the accuracy of the voice control and the BLEU score of the translation 5 . In the baseline, our system tended to generate a passive sentence compared to the voice distribution of the reference 2 Even if the root phrase of the Japanese sentence was semantically different from the root of the English sentence, we still checked the voice of the root of the English sentence without considering the meanings. 3 https://radimrehurek.com/gensim/models/word2vec.html 4 We did not perform any processing of unknown words because we focused on the control of the voice. 5 In this experiment, the BLEU score was calculated before the detokenization because we focused on the voice controlling. We submitted our system for the crowdsourcing evaluation after the detokenization. 206
ecause the number of passive sentences was greater than that of the active sentences in the training data. The accuracy of the baseline was calculated as the agreement between the voice of the generated sentence and that of the reference. ALL_ACTIVE and ALL_PASSIVE demonstrated that the voice could be controlled with high performance. The BLEU score became lower than the baseline because some sentences were transformed into different voices regardless of the contexts and voice distribution. In other words, active sentences in the test data included sentences whose root verb of the reference was an intransitive verb. Even in that case, we forced the voice of the generated sentence to become passive in ALL_PASSIVE. As a result, the voice of some sentences did not become passive, compared to other sentences that were controlled to become passive sentences if not natural. REFERENCE achieved the highest accuracy, and its voice distribution was close to that of the references. As mentioned earlier, the voice of REFERENCE was more natural than that of ALL_ACTIVE or ALL_PASSIVE. We obtained a 0.73-point improvement in the BLEU score compared to the baseline 6 . Therefore, we found that there is room for improvement if we can correctly predict the voice of the reference. PREDICT used the labels predicted from the voice distribution. It tended to generate a passive sentence compared to the baseline. The controlling accuracy was 87.5% because the voice distributions were skewed in many verbs. However, the agreement rate between the predicted and the reference voices was 63.7%. Therefore, PREDICT failed to predict the voice of the reference, especially with high-frequency verbs, resulting in decrease in the BLEU score. We leave the prediction of the voice of references as a future work. We show the output examples in Table 3. Examples 1, 2, and 3 are the success cases, whereas Examples 4 and 5 are the failure cases. Examples 1 and 2 showed that the voice of the generated sentence was correctly controlled. When a passive sentence was changed into an active sentence, a subject was needed. Both examples generated adequate subjects depending on the context. In Example 3, although the voice was controlled, the subject and object were not exchanged. Besides this example, there were many sentences that persisted the “beverb + verb in past participle form” structure when adding the label was added. For example, the “... can be done ...” structure was changed into the “... is able to be done ...” structure. In this experiment, we did not evaluate whether the subject and object were exchanged, but it may be necessary to distinguish these patterns for the purpose of improving the coherence of the discourse structure. In Example 4, it was impossible to make a passive sentence because the root verb in the target sentence should be an intransitive verb. Most of the active sentences in ALL_PASSIVE should stay active sentences that used intransitive verbs. Like Example 3, there were many sentences that were successfully controlled by using the “be found to be ...” structure when an intransitive verb was included as a root verb. Example 5 showed the case wherein the voice could not be controlled despite the attempt to control it to the active voice. The frequency of the voice of the verb “detect” in the training data consisted of 468 active-voice sentences and 2,858 passive sentences. When we forced the voice of the generated sentence to become active, the result of generation tended to fail sometimes if we input the verb that had few examples of active sentences in the training data. The subject should be generated if we forced the voice of the generated sentence to become active. However, the encoder-decoder model did not know what to generate as a subject if the training data had only a few examples of an active sentence for that verb. On the other hand, when we forced the voice of the generated sentence to become passive, we failed to find any tendencies of this type of the failure. We would like to do some additional investigation on the tendency of this result. 4.2 Experiments with Predicated Voice: TMU at WAT 2016 Table 4 shows the results of two methods submitted for the shared task at WAT 2016 (Nakazawa et al., 2016a). The BLEU, RIBES (Isozaki et al., 2010), and AMFM (Banchs et al., 2015) were calculated 6 We were not able to submit REFERENCE for the human evaluation because we were not allowed to look at the references in WAT 2016. 207
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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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Page 171 and 172: Integrating empty category detectio
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Page 179 and 180: Kenneth Heafield. 2011. Kenlm: Fast
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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 225 and 226: Chinese-to-Japanese Patent Machine
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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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