December 11-16 2016 Osaka Japan

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Figure 14: Official evaluation results of IITB-HJ. 26
Annotator A Annotator B all weighted SYSTEM ID average variance average variance average κ κ ASPEC-JE Kyoto-U 1 3.760 0.682 4.010 0.670 3.885 0.205 0.313 NAIST 1 3.705 0.728 3.950 0.628 3.828 0.257 0.356 NICT-2 3.025 0.914 3.360 0.740 3.193 0.199 0.369 ASPEC-EJ Kyoto-U 1 3.970 0.759 4.065 0.851 4.018 0.346 0.494 bjtu nlp 3.800 0.980 3.625 1.364 3.713 0.299 0.509 NICT-2 3.745 0.820 3.670 0.931 3.708 0.299 0.486 Online A 3.600 0.770 3.590 0.862 3.595 0.273 0.450 ASPEC-JC Kyoto-U 1 3.995 1.095 3.755 1.145 3.875 0.203 0.362 bjtu nlp 3.920 1.054 3.340 1.244 3.630 0.154 0.290 NICT-2 2.940 1.846 2.850 1.368 2.895 0.237 0.477 ASPEC-CJ Kyoto-U 1 4.245 1.045 3.635 1.232 3.940 0.234 0.341 UT-KAY 1 3.995 1.355 3.370 1.143 3.683 0.152 0.348 bjtu nlp 3.950 1.278 3.330 1.221 3.640 0.179 0.401 JPC-JE bjtu nlp 4.085 0.798 4.505 0.580 4.295 0.254 0.393 Online A 3.910 0.652 4.300 0.830 4.105 0.166 0.336 NICT-2 1 3.705 1.118 4.155 1.011 3.930 0.277 0.458 JPC-EJ NICT-2 1 4.025 0.914 4.510 0.570 4.268 0.234 0.412 bjtu nlp 3.920 0.924 4.470 0.749 4.195 0.151 0.340 JAPIO 1 4.055 0.932 4.250 0.808 4.153 0.407 0.562 JPC-JC bjtu nlp 3.485 1.720 3.015 1.755 3.250 0.274 0.507 NICT-2 1 3.230 1.867 2.935 1.791 3.083 0.307 0.492 S2T 2.745 2.000 2.680 1.838 2.713 0.305 0.534 JPC-CJ ntt 1 3.605 1.889 3.265 1.765 3.435 0.263 0.519 JAPIO 1 3.385 1.947 3.085 2.088 3.235 0.365 0.592 NICT-2 1 3.410 1.732 3.045 1.883 3.228 0.322 0.518 JPC-KJ JAPIO 1 4.580 0.324 4.660 0.304 4.620 0.328 0.357 EHR 1 4.510 0.380 4.615 0.337 4.563 0.424 0.478 Online A 4.380 0.466 4.475 0.409 4.428 0.517 0.574 BPPT-IE Online A 2.675 0.489 3.375 1.564 3.025 0.048 0.187 Sense 1 2.685 0.826 2.420 1.294 2.553 0.242 0.408 IITB-EN-ID 2.485 0.870 2.345 1.216 2.415 0.139 0.324 BPPT-EI Online A 2.890 1.778 3.375 1.874 3.133 0.163 0.446 Sense 1 2.395 1.059 2.450 1.328 2.423 0.305 0.494 IITB-EN-ID 2.185 1.241 2.360 1.130 2.273 0.246 0.477 IITB-EH Online A 3.200 1.330 3.525 1.189 3.363 0.103 0.155 EHR 2.590 1.372 1.900 0.520 2.245 0.136 0.263 IITP-MT 2.350 1.198 1.780 0.362 2.065 0.066 0.164 IITB-HJ Online A 1.955 1.563 2.310 0.664 2.133 0.120 0.287 EHR 1 1.530 1.049 2.475 0.739 2.003 0.055 0.194 Table 8: JPO adequacy evaluation results in detail. 27
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: Figure 13: Official evaluation resu
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 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
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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 ]
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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
Page 229 and 230:
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
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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