December 11-16 2016 Osaka Japan

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Verb # Active # Passive # Total show 21,703 10,441 (32.5%) 32,144 describe 12,300 17,474 (58.7%) 29,774 explain 7,210 13,073 (64.5%) 20,283 introduce 6,030 9,167 (60.3%) 15,197 examine 3,795 11,100 (74.5%) 14,895 find 2,367 12,507 (84.1%) 14,874 observe 1,000 12,626 (92.7%) 13,626 All verbs 383,052 444,451 (53.7%) 827,503 Table 1: Number of occurrences of each voice in high-frequency verbs. Figure 1: Flow of the automatic annotation for training an NMT. Similar to Sennrich et al. (2016), this paper reports on our attempt to control the voice of a sentence generated by an encoder-decoder model. At the preprocessing phase, we determined the voice of the root phrase in the target side by parsing and added it to the end of the source sentence as a voice label. At the training phase, we trained an attentional encoder-decoder model by using the preprocessed source data. Lastly, we controlled the voice of the generated sentence by adding a voice label to the source sentence at the test phase. We tested several configurations: (1) controlling all sentences to active/passive voices, (2) controlling each sentence to the same voice as the reference sentence, and (3) predicting the voice using only the source sentence. The result showed that we were able to control the voice of the generated sentence with 85.0% accuracy on average. In the evaluation of the Japanese-English translation, we obtained a 0.73-point improvement in BLEU score compared to the NMT baseline, in the case of using the voice information of the references. 2 Controlling Voice in Neural Machine Translation 2.1 The Control Framework In Japanese-English translation, the voices of the source and target sentences sometimes differ because the use of the verbs between the source and the target languages is different. In particular, English uses the passive voice to change the word order of a sentence for object topicalization to encode the information structure. Thus, it is beneficial to control the syntactic structure of the English sentences for discourse-aware machine translation. Moreover, if the voice of the generated sentence fluctuates at each sentence, it is difficult to train a translation model consistently. In this paper, we attempt to add a ability of voice control to an encoder-decoder model, based on Sennrich et al. (2016), which controls the honorifics in English-German neural machine translation. They restricted the honorifics of the generated sentence by adding the honorific information to the source side. Instead of the honorific information, we extracted the voice information of the target sentence as a gold standard label to annotate the source sentence. At the test phase, we specified the voice of the generated 204
Figure 2: Flow of the voice prediction for testing an NMT. sentence, and instructed the model to translate along with it. In the following experiment, we used the attentional encoder-decoder model by Bahdanau et al. (2015). It is the same model that Sennrich et al. (2016) used. This model uses a bi-directional RNN as an encoder with attention structure. The proposed method can be adapted to any sequence-to-sequence model because it does not depend on the network structure. 2.2 Automatic Labeling of the Voice for Training The performance of this method depends on the annotation performance of the voice at the training phase. Figure 1 shows the flow of the automatic annotation for training the attentional encoder-decoder model. We recognized the voice of the target (English) sentence by parsing. Then, the result of the parsing was checked to determine whether the root was a verb in the past participle form or not and whether it had a be-verb in the children or not. If both conditions were satisfied, the target sentence was recognized as being in the passive voice; otherwise, it was in the active voice 1 . For the voice controlling, we added a special token, or , as a word to the end of the sentence, which became the input to the encoder. The special token, or , encoded the voice of the root of the target sentence. The decoder considered only these tokens to determine the voice of the target sentence. For simplicity, we annotated only one voice for each sentence. In other words, if the sentence was a complex sentence, we selected the root verb for annotation. How the non-root verb must be treated in order to obtain the consistency of the document expression will be studied in a future work. 2.3 Voice Prediction for Testing This study assumed that the voice label was determined in advance, but it was sometimes difficult to determine which label was suitable just from the source sentence alone. Even in this case, we had to add a voice label to the end of the source sentence to generate a target sentence because the proposed method necessarily uses a voice label. Thus, we attempted to predict the voice for each sentence. Figure 2 shows the flow of the voice prediction. We investigated the voice distribution of the English verb in each root phrase of the Japanese side in the training data to predict the voice of the generated sentence. At the test phase, we also obtained the root phrase of the Japanese sentence. If the root phrase was included in the training data, we added the majority label of the voice distribution in the training data as a predicted label. If the root phrase was not in the training data, the voice label was . 3 Experiments We conducted two types of evaluations: evaluation of the controlling accuracy and evaluation of the machine translation quality. We tested the following four patterns of labeling the voice features to evaluate 1 Strictly speaking, we checked whether the target sentence was in the passive voice or not, but we did not distinguish “not in passive voice” from “active voice.” 205
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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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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 217: Controlling the Voice of a Sentence
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December 11-16 2016 Osaka Japan

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