December 11-16 2016 Osaka Japan

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1.800 1.600 1.400 1.200 1.000 0.800 0.600 0.400 0.200 0.000 -7.000 -6.000 -5.000 -4.000 -3.000 -2.000 -1.000 0.000 Figure 4: Scatter plot of Thai tokens. X-axis is the logarithmic probability of tokens; Y-axis is the entropy on corresponding Laotian tokens. 1.800 1.600 1.400 1.200 1.000 0.800 0.600 0.400 0.200 0.000 -7.000 -6.000 -5.000 -4.000 -3.000 -2.000 -1.000 0.000 Figure 5: Scatter plot of Laotian tokens. X-axis is the logarithmic probability of tokens; Y-axis is the entropy on corresponding Thai tokens. 154
1.800 1.600 1.400 1.200 1.000 0.800 0.600 0.400 0.200 0.000 -9.000 -8.000 -7.000 -6.000 -5.000 -4.000 -3.000 -2.000 -1.000 0.000 Figure 6: Scatter plot of <strong>Japan</strong>ese tokens. X-axis is the logarithmic probability of tokens; Y-axis is the entropy on corresponding Korean tokens. 1.800 1.600 1.400 1.200 1.000 0.800 0.600 0.400 0.200 0.000 -9.000 -8.000 -7.000 -6.000 -5.000 -4.000 -3.000 -2.000 -1.000 0.000 Figure 7: Scatter plot of Korean tokens. X-axis is the logarithmic probability of tokens; Y-axis is the entropy on corresponding <strong>Japan</strong>ese tokens. 155
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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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Page 35 and 36:
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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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Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
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
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 and 130: where, f, p and e means a source, p
Page 131 and 132: former system can translate more th
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: -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3
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
Page 181 and 182: Figure 1: Overview of KyotoEBMT. Th
Page 183 and 184: proportionally slower to compute (a
Page 185 and 186: 3.6 Ensembling Ensembling has previ
Page 187 and 188: when Chinese is also the language m
Page 189 and 190: Character-based Decoding in Tree-to
Page 191 and 192: where W s ∈ R d×d is a matrix an
Page 193 and 194: Model BLEU (BP) RIBES Character-bas
Page 195 and 196: The word-based NMT models cannot us
Page 197 and 198: Razvan Pascanu, Tomas Mikolov, and
Page 199 and 200: governs the grammaticality of the t
Page 201 and 202: 2.1 Quantization and Binarization o
Page 203 and 204: 3 Results Team Other Resources Syst
Page 205 and 206: Peter F Brown, John Cocke, Stephen
Page 207 and 208: Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210: Language Sentence Chinese 该 / 钽
Page 211 and 212: Chinese or Japanese sentences Chine
Page 213 and 214: 4 Experiments and Results 4.1 Chine
Page 215 and 216: 6 Conclusion and Future Work In thi
Page 217 and 218: 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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