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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 5: Official evaluation resul
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Figure 7: Official evaluation resul
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Figure 9: 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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SYSTEM ID ID METHOD OTHER RESOURCES
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SYSTEM ID ID METHOD OTHER BLEU RIBE
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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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