December 11-16 2016 Osaka Japan

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LangPair Train Dev DevTest Test ASPEC-JE 3,008,500 1,790 1,784 1,812 ASPEC-JC 672,315 2,090 2,148 2,107 Table 1: Statistics for ASPEC. 2 Dataset WAT uses the Asian Scientific Paper Excerpt Corpus (ASPEC) 1 , JPO Patent Corpus (JPC) 2 , BPPT Corpus 3 and IIT Bombay English-Hindi Corpus (IITB Corpus) 4 as the dataset. 2.1 ASPEC ASPEC is constructed by the Japan Science and Technology Agency (JST) in collaboration with the National Institute of Information and Communications Technology (NICT). It consists of a Japanese- English scientific paper abstract corpus (ASPEC-JE), which is used for J↔E subtasks, and a Japanese- Chinese scientific paper excerpt corpus (ASPEC-JC), which is used for J↔C subtasks. The statistics for each corpus are described in Table1. 2.1.1 ASPEC-JE The training data for ASPEC-JE was constructed by the NICT from approximately 2 million Japanese- English scientific paper abstracts owned by the JST. Because the abstracts are comparable corpora, the sentence correspondences are found automatically using the method from (Utiyama and Isahara, 2007). Each sentence pair is accompanied by a similarity score and the field symbol. The similarity scores are calculated by the method from (Utiyama and Isahara, 2007). The field symbols are single letters A-Z and show the scientific field for each document 5 . The correspondence between the symbols and field names, along with the frequency and occurrence ratios for the training data, are given in the README file from ASPEC-JE. The development, development-test and test data were extracted from parallel sentences from the Japanese-English paper abstracts owned by JST that are not contained in the training data. Each data set contains 400 documents. Furthermore, the data has been selected to contain the same relative field coverage across each data set. The document alignment was conducted automatically and only documents with a 1-to-1 alignment are included. It is therefore possible to restore the original documents. The format is the same as for the training data except that there is no similarity score. 2.1.2 ASPEC-JC ASPEC-JC is a parallel corpus consisting of Japanese scientific papers from the literature database and electronic journal site J-STAGE of JST that have been translated to Chinese with permission from the necessary academic associations. The parts selected were abstracts and paragraph units from the body text, as these contain the highest overall vocabulary coverage. The development, development-test and test data are extracted at random from documents containing single paragraphs across the entire corpus. Each set contains 400 paragraphs (documents). Therefore, there are no documents sharing the same data across the training, development, development-test and test sets. 2.2 JPC JPC was constructed by the Japan Patent Office (JPO). It consists of a Chinese-Japanese patent description corpus (JPC-CJ), Korean-Japanese patent description corpus (JPC-KJ) and English-Japanese patent description corpus (JPC-EJ) with four sections, which are Chemistry, Electricity, Mechanical engineering, and Physics, based on International Patent Classification (IPC). Each corpus is separated into 1 http://lotus.kuee.kyoto-u.ac.jp/ASPEC/ 2 http://lotus.kuee.kyoto-u.ac.jp/WAT/patent/index.html 3 http://orchid.kuee.kyoto-u.ac.jp/WAT/bppt-corpus/index.html 4 http://www.cfilt.iitb.ac.in/iitb parallel/index.html 5 http://opac.jst.go.jp/bunrui/index.html 2
LangPair Train Dev DevTest Test JPC-CJ 1,000,000 2,000 2,000 2,000 JPC-KJ 1,000,000 2,000 2,000 2,000 JPC-EJ 1,000,000 2,000 2,000 2,000 Table 2: Statistics for JPC. LangPair Train Dev DevTest Test BPPT-IE 50,000 400 400 400 Table 3: Statistics for BPPT Corpus. training, development, development-test and test data, which are sentence pairs. This corpus was used for patent subtasks C↔J, K↔J and E↔J. The statistics for each corpus are described in Table2. The Sentence pairs in each data were randomly extracted from a description part of comparable patent documents under the condition that a similarity score between sentences is greater than or equal to the threshold value 0.05. The similarity score was calculated by the method from (Utiyama and Isahara, 2007) as with ASPEC. Document pairs which were used to extract sentence pairs for each data were not used for the other data. Furthermore, the sentence pairs were extracted to be same number among the four sections. The maximize number of sentence pairs which are extracted from one document pair was limited to 60 for training data and 20 for the development, development-test and test data. The training data for JPC-CJ was made with sentence pairs of Chinese-Japanese patent documents published in 2012. For JPC-KJ and JPC-EJ, the training data was extracted from sentence pairs of Korean-Japanese and English-Japanese patent documents published in 2011 and 2012. The development, developmenttest and test data for JPC-CJ, JPC-KJ and JPC-EJ were respectively made with 100 patent documents published in 2013. 2.3 BPPT Corpus BPPT Corpus was constructed by Badan Pengkajian dan Penerapan Teknologi (BPPT). This corpus consists of a Indonesian-English news corpus (BPPT-IE) with five sections, which are Finance, International, Science and Technology, National, and Sports. These data come from Antara News Agency. This corpus was used for newswire subtasks I↔E. The statistics for each corpus are described in Table3. 2.4 IITB Corpus IIT Bombay English-Hindi corpus contains English-Hindi parallel corpus (IITB-EH) as well as monolingual Hindi corpus collected from a variety of existing sources and corpora developed at the Center for Indian Language Technology, IIT Bombay over the years. This corpus was used for mixed domain subtasks H↔E. Furthermore, mixed domain subtasks H↔J were added as a pivot language task with a parallel corpus created using openly available corpora (IITB-JH) 6 . Most sentence pairs in IITB-JH come from the Bible corpus. The statistics for each corpus are described in Table4. 3 Baseline Systems Human evaluations were conducted as pairwise comparisons between the translation results for a specific baseline system and translation results for each participant’s system. That is, the specific baseline system was the standard for human evaluation. A phrase-based statistical machine translation (SMT) system was adopted as the specific baseline system at WAT 2016, which is the same system as that at WAT 2014 and WAT 2015. In addition to the results for the baseline phrase-based SMT system, we produced results for the baseline systems that consisted of a hierarchical phrase-based SMT system, a string-to-tree syntax-based 6 http://lotus.kuee.kyoto-u.ac.jp/WAT/Hindi-corpus/WAT2016-Ja-Hi.zip 3
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: Overview of the 3rd Workshop on Asi
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 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
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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
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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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