December 11-16 2016 Osaka Japan

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5 All important information is transmitted correctly. (100%) 4 Almost all important information is transmitted correctly. (80%–) 3 More than half of important information is transmitted correctly. (50%–) 2 Some of important information is transmitted correctly. (20%–) 1 Almost all important information is NOT transmitted correctly. (–20%) Table 6: The JPO adequacy criterion 5.1.4 Confidence Interval Estimation There are several ways to estimate a confidence interval. We chose to use bootstrap resampling (Koehn, 2004) to estimate the 95% confidence interval. The procedure is as follows: 1. randomly select 300 sentences from the 400 human evaluation sentences, and calculate the Pairwise score of the selected sentences 2. iterate the previous step 1000 times and get 1000 Pairwise scores 3. sort the 1000 scores and estimate the 95% confidence interval by discarding the top 25 scores and the bottom 25 scores 5.2 JPO Adequacy Evaluation The participants’ systems, which achieved the top 3 highest scores among the pairwise evaluation results of each subtask 22 , were also evaluated with the JPO adequacy evaluation. The JPO adequacy evaluation was carried out by translation experts with a quality evaluation criterion for translated patent documents which the Japanese Patent Office (JPO) decided. For each system, two annotators evaluate the test sentences to guarantee the quality. 5.2.1 Evaluation of Sentences The number of test sentences for the JPO adequacy evaluation is 200. The 200 test sentences were randomly selected from the 400 test sentences of the pairwise evaluation. The test sentence include the input sentence, the submitted system’s translation and the reference translation. 5.2.2 Evaluation Criterion Table 6 shows the JPO adequacy criterion from 5 to 1. The evaluation is performed subjectively. “Important information” represents the technical factors and their relationships. The degree of importance of each element is also considered to evaluate. The percentages in each grade are rough indications for the transmission degree of the source sentence meanings. The detailed criterion can be found on the JPO document (in Japanese) 23 . 6 Participants List Table 7 shows the list of participants for WAT2016. This includes not only Japanese organizations, but also some organizations from outside Japan. 15 teams submitted one or more translation results to the automatic evaluation server or human evaluation. 22 The number of systems varies depending on the subtasks. 23 http://www.jpo.go.jp/shiryou/toushin/chousa/tokkyohonyaku hyouka.htm 10
ASPEC JPC BPPT IITBC pivot Team ID Organization JE EJ JC CJ JE EJ JC CJ JK KJ IE EI HE EH HJ JH NAIST (Neubig, 2016) Nara Institute of Science and Technology ✓ Kyoto-U (Cromieres et al., 2016) Kyoto University ✓ ✓ ✓ ✓ TMU (Yamagishi et al., 2016) Tokyo Metropolitan University ✓ bjtu nlp (Li et al., 2016) Beijing Jiaotong University ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Sense (Tan, 2016) Saarland University ✓ ✓ NICT-2 (Imamura and Sumita, 2016) National Institute of Information and Communication Technology ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ WASUIPS (Yang and Lepage, 2016) Waseda University ✓ EHR (Ehara, 2016) Ehara NLP Research Laboratory ✓ ✓ ✓ ✓ ✓ ✓ ntt (Sudoh and Nagata, 2016) NTT Communication Science Laboratories ✓ TOKYOMT (Shu and Miura, 2016) Weblio, Inc. ✓ IITB-EN-ID (Singh et al., 2016) Indian Institute of Technology Bombay ✓ ✓ JAPIO (Kinoshita et al., 2016) Japan Patent Information Organization ✓ ✓ ✓ ✓ ✓ IITP-MT (Sen et al., 2016) Indian Institute of Technology Patna ✓ UT-KAY (Hashimoto et al., 2016) University of Tokyo ✓ UT-AKY (Eriguchi et al., 2016) University of Tokyo ✓ Table 7: List of participants who submitted translation results to WAT2016 and their participation in each subtasks. 11
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: • Use of other resources in addit
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 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
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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
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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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December 11-16 2016 Osaka Japan

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