December 11-16 2016 Osaka Japan

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SYSTEM ID ID METHOD OTHER RESOURCES BLEU RIBES AMFM Pair SYSTEM DESCRIPTION Online A 1031 Other YES 21.37 0.714537 0.621100 +44.75 Online A (2016) Online B 1048 Other YES 15.58 0.683214 0.590520 +14.00 Online B (2016) SMT Phrase 1054 SMT NO 10.32 0.638090 0.574850 0.00 Phrase-based SMT Table 33: IITB-HE submissions SYSTEM ID ID METHOD OTHER RESOURCES BLEU RIBES AMFM Pair SYSTEM DESCRIPTION SMT Phrase 1252 SMT NO 10.790000 0.651166 0.660860 —– Phrase-based SMT Online A 1032 Other YES 18.720000 0.716788 0.670660 +57.25 Online A (2016) Online B 1047 Other YES 16.970000 0.691298 0.668450 +42.50 Online B (2016) EHR 1 1166 SMT NO 11.750000 0.671866 0.650750 0.00 PBSMT with preordering (DL=6) IITP-MT 1 1185 SMT YES 13.710000 0.688913 0.657330 +4.75 IITP-MT System1 Table 34: IITB-EH submissions 42
SYSTEM ID ID METHOD OTHER BLEU RIBES AMFM RESOURCES juman kytea mecab juman kytea mecab juman kytea mecab Pair SYSTEM DESCRIPTION SMT Phrase 1251 SMT NO 2.05 4.17 2.42 0.440122 0.496402 0.461763 0.360910 0.360910 0.360910 —– Phrase-based SMT Online A 1064 Other YES 6.60 10.42 7.47 0.565109 0.597863 0.576725 0.495270 0.495270 0.495270 +39.75 Online A (2016) Online B 1065 Other YES 5.70 8.91 6.38 0.560486 0.589558 0.571670 0.471450 0.471450 0.471450 +17.75 Online B (2016) EHR 1 1167 SMT YES 7.81 10.12 8.11 0.579285 0.617098 0.588723 0.468140 0.468140 0.468140 +13.75 PBSMT with phrase table pivoting and pivot language (en) reordering. User dictionary and TED based LM are used. EHR 2 1179 SMT YES 7.66 9.80 7.95 0.585953 0.618106 0.597490 0.473120 0.473120 0.473120 +10.00 PBSMT with sentence level pivoting and pivot language (en) reordering. User dictionary and TED based LM are used. Table 35: IITB-HJ submissions SYSTEM ID ID METHOD OTHER RESOURCES BLEU RIBES AMFM Pair SYSTEM DESCRIPTION SMT Phrase 1253 SMT NO 1.590000 0.399448 0.467980 —– Phrase-based SMT Online B 1066 Other YES 4.210000 0.488631 0.528220 +51.75 Online B (2016) Online A 1067 Other YES 4.430000 0.495349 0.525690 +54.50 Online A (2016) Table 36: IITB-JH submissions 43
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 37 and 38: Figure 11: Official evaluation resu
Page 39 and 40: Figure 13: Official evaluation resu
Page 41 and 42: Annotator A Annotator B all weighte
Page 43 and 44: Kyoto-U 2 bjtu nlp UT-KAY 1 UT-KAY
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: SYSTEM ID ID METHOD OTHER RESOURCES
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
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
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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
Page 181 and 182:
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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