December 11-16 2016 Osaka Japan

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For all tasks using the reordering, the number of iterations and average values of Kendall’s tau which are obtained by the last iteration are listed in Table 4. The Kendall’s tau of en-ja and HIN- DENeh-hi tasks are rather low than that of zh-ja and JPCzh-ja tasks. Task Iteration Kendall's tau en-ja 4 0.7655 zh-ja 4 0.9083 JPCzh-ja 8 0.8788 HINDENen-hi 4 0.8398 Table 4: Number of iterations and average values of Kendall’s tau for the tasks 4.2 Results of system combination For JPCzh-ja task, we combine three systems: word based SMT, character based SMT and RBMT plus SPE. We compare translation accuracy using devtest set by BLEU and RIBES. Table 5 shows these scores of several combinations of the systems. We can see our system combination is effective. 4.3 Translation evaluation results No. System BLEU RIBES 1 word based SMT 42.07 82.91 2 char based SMT 41.82 83.03 3 RBMT + SPE 41.61 82.42 4 to combine 1 and 2 42.13 83.13 5 to combine 1, 2 and 3 42.42 83.16 Table 5: Effect of system combination in JPCzh-ja task We submitted 12 systems’ outputs to the evaluation site. The system descriptions are summarized in Table 6. Official evaluation results of our systems by the organizer (Nakazawa et al., 2016) are listed in Table 7. Evaluation results of the top ranked system are also listed in the table for the comparison. For JPCzh-ja task, adding RBMT plus SPE increase BLEU score but decrease RIBES, AMFM and HUMAN scores. For JPCko-ja task, deleting parentheses surrounding a number in Korean side increase three automatic scores. Oppositely, adding parentheses surrounding a number in Japanese side increase HUMAN score. For HINDENhi-ja task, table level pivoting has higher BLEU and HUMAN score and lower RIBES and AMFM scores than sentence level pivoting. Comparing system 2 and 3 of HINDENhi-ja task, reordering of pivot language (English) is effective with three automatic scores. Pivoting method (system 1, 2 and 3) substantially increases automatic scores compared with the direct method (system 4). However, evaluation scores of this task are largely low compared with the scores of other tasks. Task System No. Word-based PBSMT Characterbased PBSMT RBMT+SPE Reordering Sentence level pivoting Table level pivoting Parenthes handling en-ja 1 ✔ ✔ zh-ja 1 ✔ ✔ ✔ JPCzh-ja 1 ✔ ✔ ✔ ✔ 2 ✔ ✔ ✔ 1 ✔ ✔ del JPCko-ja 2 ✔ ✔ add & del 3 ✔ ✔ add HINDENen-hi 1 ✔ ✔ 1 ✔ ✔ ✔ HINDENhi-ja 2 ✔ ✔ ✔ 3 ✔ ✔ 4 ✔ Table 6: System descriptions of the tasks Three systems are evaluated by the JPO adequacy. The system for JPCko-ja task has high JPO adequacy score and systems for HINDENen-hi and HINDENhi-ja tasks have low JPO adequacy score. The 116
former system can translate more than 90% of sentences with 4 or 5 JPO adequacy score, however, the latter systems can only translate 10 or 20% of sentences with 4 or 5 JPO adequacy score (see Figure 2). Task System No. BLEU RIBES AMFM HUMAN HUMAN (top rank) JPO adq. JPO adq. (top rank) en-ja 1 31.32 0.7599 0.7467 39.000 55.250 --- 4.02 zh-ja 1 39.75 0.8437 0.7695 32.500 63.750 --- 3.94 JPCzh-ja 1 41.05 0.8270 0.7350 35.500 --- 46.500 2 40.95 0.8280 0.7451 39.000 --- 3.44 1 71.51 0.9447 0.8664 -3.000 --- JPCko-ja 2 68.78 0.9411 0.8517 --- 21.750 --- 4.62 3 62.33 0.9271 0.8180 21.750 4.56 HINDENen-hi 1 11.75 0.6719 0.6508 0.000 57.250 2.48 2.55 1 7.81 0.5793 0.4681 13.750 2.00 HINDENhi-ja 2 7.66 0.5860 0.4731 10.000 --- 39.750 3 7.47 0.5823 0.4549 --- --- 2.13 4 2.36 0.4402 0.3628 --- --- Table 7: Evaluation results (Segmenter for ja is JUMAN) ( % ) 100 90 80 70 60 50 40 30 20 1 2 3 4 5 10 0 JPCko-ja HINDENen-hi HINDENhi-ja Figure 2: Evaluation results by the JPO adequacy 5 Conclusion System descriptions, experimental settings and experimental results of EHR group were described. We participate in the 6 tasks and submitted 12 systems’ outputs. We can recognize our translation techniques are effective. They are iterative reordering, system combination and pivoting with reordering. Several remaining issues are as follows. To improve parsing accuracy such that reordering accuracy be higher. To improve English-Hindi and Hindi-Japanese translation accuracy that are largely lower than that of other language pairs. To challenge machine translations for other Asian languages such as Indonesian, Thai, Vietnamese, Mongolian and so on. Reference Mauro Cettolo, Christian Girardi, and Marcello Federico. 2012. WIT 3 : Web Inventory of Transcribed and Translated Talks. Proceedings of the 16th EAMT Conference, pages 261-168. Pi-Chuan Chang, Michel Galley and Chris Manning. 2008. Optimizing Chinese Word Segmentation for Machine Translation Performance. Proceedings of the Third Workshop on Statistical Machine Translation, pages 224- 232. 117
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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/
Page 79 and 80: Table 5: Number of fluency errors i
Page 81 and 82: Kenneth Heafield. 2011. Kenlm: Fast
Page 83 and 84: under-studied. MorphInd, a morphana
Page 85 and 86: dimensions of 150 units in second h
Page 87 and 88: distribution as received in JPO ade
Page 89 and 90: Domain Adaptation and Attention-Bas
Page 91 and 92: Once s M , which represents the ent
Page 93 and 94: 3.4.2 Ensemble of Attention Scores
Page 95 and 96: Type Count Ratio (A) Correct 76 30.
Page 97 and 98: Minh-Thang Luong, Hieu Pham, and Ch
Page 99 and 100: Pair 1 Japanese [[ A ][ B ][ C ]
Page 101 and 102: ID n-grams len freq m1 prevent, | 1
Page 103 and 104: prediction accuracy with respect to
Page 105 and 106: Reference: [ A To provide] [ B a to
Page 107 and 108: 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: where, f, p and e means a source, p
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 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
Page 167 and 168: -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3
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
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Figure 1: Overview of KyotoEBMT. Th
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proportionally slower to compute (a
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3.6 Ensembling Ensembling has previ
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when Chinese is also the language m
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Character-based Decoding in Tree-to
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where W s ∈ R d×d is a matrix an
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Model BLEU (BP) RIBES Character-bas
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The word-based NMT models cannot us
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Razvan Pascanu, Tomas Mikolov, and
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governs the grammaticality of the t
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2.1 Quantization and Binarization o
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3 Results Team Other Resources Syst
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Peter F Brown, John Cocke, Stephen
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Yonghui Wu, Mike Schuster, Zhifeng
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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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