- Page 1 and 2: WAT 2016 The 3rd Workshop on Asian
- Page 3: Preface Many Asian countries are ra
- Page 6 and 7: Technical Collaborators Luis Fernan
- Page 9 and 10: Table of Contents Overview of the 3
- Page 11 and 12: Conference Program December 12, 201
- Page 13 and 14: December 12, 2016 (continued) 12:00
- Page 15 and 16: Overview of the 3rd Workshop on Asi
- Page 17 and 18: LangPair Train Dev DevTest Test JPC
- Page 19 and 20: ASPEC JPC IITB BPPT pivot System ID
- Page 21 and 22: 3.6 Tree-to-String Syntax-based SMT
- Page 23 and 24: • Use of other resources in addit
- Page 25 and 26: ASPEC JPC BPPT IITBC pivot Team ID
- Page 27 and 28: ut the adequacy is worse. As for AS
- Page 29 and 30: Figure 3: Official evaluation resul
- Page 31 and 32: Figure 5: Official evaluation resul
- Page 33 and 34: Figure 7: Official evaluation resul
- Page 35 and 36: Figure 9: Official evaluation resul
- Page 37 and 38: Figure 11: Official evaluation resu
- Page 39 and 40: Figure 13: Official evaluation resu
- Page 41: Annotator A Annotator B all weighte
- Page 45 and 46: Online B IITP-MT EHR Online A ≫
- Page 47 and 48: SYSTEM ID ID METHOD OTHER RESOURCES
- Page 49 and 50: SYSTEM ID ID METHOD OTHER BLEU RIBE
- Page 51 and 52: SYSTEM ID ID METHOD OTHER RESOURCES
- Page 53 and 54: SYSTEM ID ID METHOD OTHER BLEU RIBE
- Page 55 and 56: SYSTEM ID ID METHOD OTHER RESOURCES
- Page 57 and 58: SYSTEM ID ID METHOD OTHER BLEU RIBE
- Page 59 and 60: Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
- Page 61 and 62: Translation of Patent Sentences wit
- Page 63 and 64: Chinese sentences contain fewer tha
- Page 65 and 66: Figure 3: NMT decoding with technic
- Page 67 and 68: Table 1: Automatic evaluation resul
- Page 69 and 70: Figure 6: Example of correct transl
- Page 71 and 72: K. Papineni, S. Roukos, T. Ward, an
- Page 73 and 74: Table 1: Examples of title, ingredi
- Page 75 and 76: text. In this section, we explain t
- Page 77 and 78: 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
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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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1.800 1.600 1.400 1.200 1.000 0.800
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Integrating empty category detectio
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3 Preordering with empty categories
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We performed the tests under two co
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the decoded sentence and the refere
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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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