December 11-16 2016 Osaka Japan

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SYSTEM ID ID METHOD OTHER RESOURCES BLEU RIBES AMFM Pair SYSTEM DESCRIPTION SMT Phrase 1020 SMT NO 67.09 0.933825 0.844950 —– Phrase-based SMT SMT Hiero 1021 SMT NO 66.52 0.932391 0.844550 -3.50 Hierarchical Phrase-based SMT RBMT C 1083 Other YES 43.26 0.872746 0.766520 —– RBMT C RBMT D 1089 Other YES 45.59 0.877411 0.765530 -53.25 RBMT D Online A 1037 Other YES 48.75 0.898976 0.791320 -21.00 Online A (2016) Online B 1068 Other YES 28.21 0.827843 0.692980 —– Online B (2016) Table 29: JPC-JK submissions SYSTEM ID ID METHOD OTHER BLEU RIBES AMFM RESOURCES juman kytea mecab juman kytea mecab juman kytea mecab Pair SYSTEM DESCRIPTION SMT Phrase 438 SMT NO 69.22 70.36 69.73 0.941302 0.939729 0.940756 0.856220 0.856220 0.856220 —– Phrase-based SMT SMT Hiero 439 SMT NO 67.41 68.65 68.00 0.937162 0.935903 0.936570 0.850560 0.850560 0.850560 +2.75 Hierarchical Phrase-based SMT RBMT A 653 Other YES 42.00 43.97 42.45 0.876396 0.873734 0.875146 0.712020 0.712020 0.712020 -7.25 RBMT A RBMT B 654 Other YES 34.74 37.51 35.54 0.845712 0.849014 0.846228 0.643150 0.643150 0.643150 —– RBMT B Online A (2015) 652 Other YES 55.05 56.84 55.46 0.909152 0.909385 0.908838 0.800460 0.800460 0.800460 +38.75 Online A (2015) Online A (2016) 1039 Other YES 54.78 56.68 55.14 0.907320 0.907652 0.906743 0.798750 0.798750 0.798750 +8.00 Online A (2016) Online B (2015) 651 Other YES 36.41 38.72 37.01 0.851745 0.852263 0.851945 0.728750 0.728750 0.728750 —– Online B (2015) EHR 1 1005 SMT YES 71.51 72.32 71.77 0.944651 0.943514 0.944606 0.866370 0.866370 0.866370 -3.00 Combination of word-based PB- SMT and character-based PBSMT with DL=0. Parentheses surrounding number in Korean sentences are deleted. EHR 2 1006 SMT YES 62.33 64.17 62.75 0.927065 0.927215 0.927017 0.818030 0.818030 0.818030 +21.75 Combination of word-based PB- SMT and character-based PBSMT with DL=0. Parentheses in Korean side and not in Japanese side are added to Japanese for training and dev sets. JAPIO 1 1206 SMT YES 68.62 69.49 68.90 0.938474 0.937066 0.938230 0.858190 0.858190 0.858190 -9.00 Phrase-based SMT + JAPIO corpus + rule-based posteditor JAPIO 2 1209 SMT YES 70.32 71.07 70.52 0.942137 0.940544 0.941746 0.863660 0.863660 0.863660 +17.50 Phrase-based SMT + JPC/JAPIO corpora + rule-based posteditor Table 30: JPC-KJ submissions 40
SYSTEM ID ID METHOD OTHER RESOURCES BLEU RIBES AMFM Pair SYSTEM DESCRIPTION SMT Phrase 971 SMT NO 24.57 0.779545 0.578310 0.00 Phrase-based SMT SMT Hiero 981 SMT NO 23.62 0.776309 0.575450 -8.25 Hierarchical Phrase-based SMT SMT S2T 982 SMT NO 22.90 0.780436 0.577210 -3.25 String-to-Tree SMT Online A 1033 Other YES 28.11 0.797852 0.607290 +49.25 Online A Online B 1052 Other YES 19.69 0.770690 0.578920 +34.50 Online B Sense 1 1171 SMT NO 25.62 0.782761 0.564500 -5.00 Baseline-C50-PBMT Sense 2 1173 SMT NO 25.97 0.787768 0.570710 -8.25 Clustercat-PBMT Table 31: BPPT-IE submissions SYSTEM ID ID METHOD OTHER RESOURCES BLEU RIBES AMFM Pair SYSTEM DESCRIPTION SMT Phrase 972 SMT NO 23.95 0.808362 0.559800 0.00 Phrase-based SMT SMT Hiero 983 SMT NO 22.64 0.796701 0.568660 -17.00 Hierarchical Phrase-based SMT SMT T2S 984 SMT NO 23.65 0.792346 0.572520 -7.75 Tree-to-String SMT Online A 1034 Other YES 24.20 0.819504 0.554720 +35.75 Online A Online B 1050 Other YES 18.09 0.789499 0.514430 +10.50 Online B Sense 1 1170 SMT NO 25.16 0.807097 0.568780 +1.25 Baseline-C50-PBMT Sense 2 1174 SMT NO 25.31 0.808484 0.571890 -2.75 Clustercat-C50-PBMT ITTB-EN-ID 1 1239 SMT NO 22.35 0.808943 0.555970 -9.25 BLNLM Table 32: BPPT-EI submissions 41
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: SYSTEM ID ID METHOD OTHER BLEU RIBE
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
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
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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.
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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)
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
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#$ #%$ #$ !""
Page 157 and 158:
This formula requires a context of
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Sentence Segmenter Parameters Dev.
Page 161 and 162:
a meaningful baseline for related r
Page 163 and 164:
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
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
Page 181 and 182:
Figure 1: Overview of KyotoEBMT. Th
Page 183 and 184:
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
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
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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
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Peter F Brown, John Cocke, Stephen
Page 207 and 208:
Yonghui Wu, Mike Schuster, Zhifeng
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Language Sentence Chinese 该 / 钽
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Chinese or Japanese sentences Chine
Page 213 and 214:
4 Experiments and Results 4.1 Chine
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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
Page 221 and 222:
ecause the number of passive senten
Page 223 and 224:
controlling the sentence length wit
Page 225 and 226:
Chinese-to-Japanese Patent Machine
Page 227 and 228:
using the reordering oracles over t
Page 229 and 230:
Conference on Natural Language Proc
Page 231 and 232:
Figure 1: Hierarchical approach wit
Page 233 and 234:
newswire test and development set o
Page 235 and 236:
Source: the rain and cold wind on W
Page 237 and 238:
Residual Stacking of RNNs for Neura
Page 239 and 240:
2.2 Generalized Minimum Bayes Risk
Page 241 and 242:
5.0 4.5 4.0 Baseline model Enlarged
Page 243:
Felix Hill, Kyunghyun Cho, Sebastie
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December 11-16 2016 Osaka Japan

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