December 11-16 2016 Osaka Japan

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3.1 Training Data We used the following data for training the SMT baseline systems. • Training data for the language model: All of the target language sentences in the parallel corpus. • Training data for the translation model: Sentences that were 40 words or less in length. (For ASPEC Japanese–English training data, we only used train-1.txt, which consists of one million parallel sentence pairs with high similarity scores.) • Development data for tuning: All of the development data. 3.2 Common Settings for Baseline SMT We used the following tools for tokenization. • Juman version 7.0 8 for Japanese segmentation. • Stanford Word Segmenter version 2014-01-04 9 (Chinese Penn Treebank (CTB) model) for Chinese segmentation. • The Moses toolkit for English and Indonesian tokenization. • Mecab-ko 10 for Korean segmentation. • Indic NLP Library 11 for Hindi segmentation. To obtain word alignments, GIZA++ and grow-diag-final-and heuristics were used. We used 5-gram language models with modified Kneser-Ney smoothing, which were built using a tool in the Moses toolkit (Heafield et al., 2013). 3.3 Phrase-based SMT We used the following Moses configuration for the phrase-based SMT system. • distortion-limit – 20 for JE, EJ, JC, and CJ – 0 for JK, KJ, HE, and EH – 6 for IE and EI • msd-bidirectional-fe lexicalized reordering • Phrase score option: GoodTuring The default values were used for the other system parameters. 3.4 Hierarchical Phrase-based SMT We used the following Moses configuration for the hierarchical phrase-based SMT system. • max-chart-span = 1000 • Phrase score option: GoodTuring The default values were used for the other system parameters. 3.5 String-to-Tree Syntax-based SMT We used the Berkeley parser to obtain target language syntax. We used the following Moses configuration for the string-to-tree syntax-based SMT system. • max-chart-span = 1000 • Phrase score option: GoodTuring • Phrase extraction options: MaxSpan = 1000, MinHoleSource = 1, and NonTermConsecSource. The default values were used for the other system parameters. 8 http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?JUMAN 9 http://nlp.stanford.edu/software/segmenter.shtml 10 https://bitbucket.org/eunjeon/mecab-ko/ 11 https://bitbucket.org/anoopk/indic nlp library 6
3.6 Tree-to-String Syntax-based SMT We used the Berkeley parser to obtain source language syntax. We used the following Moses configuration for the baseline tree-to-string syntax-based SMT system. • max-chart-span = 1000 • Phrase score option: GoodTuring • Phrase extraction options: MaxSpan = 1000, MinHoleSource = 1, MinWords = 0, NonTermConsecSource, and AllowOnlyUnalignedWords. The default values were used for the other system parameters. 4 Automatic Evaluation 4.1 Procedure for Calculating Automatic Evaluation Score We calculated automatic evaluation scores for the translation results by applying three metrics: BLEU (Papineni et al., 2002), RIBES (Isozaki et al., 2010) and AMFM (Banchs et al., 2015). BLEU scores were calculated using multi-bleu.perl distributed with the Moses toolkit (Koehn et al., 2007); RIBES scores were calculated using RIBES.py version 1.02.4 12 ; AMFM scores were calculated using scripts created by technical collaborators of WAT2016. All scores for each task were calculated using one reference. Before the calculation of the automatic evaluation scores, the translation results were tokenized with word segmentation tools for each language. For Japanese segmentation, we used three different tools: Juman version 7.0 (Kurohashi et al., 1994), KyTea 0.4.6 (Neubig et al., 2011) with Full SVM model 13 and MeCab 0.996 (Kudo, 2005) with IPA dictionary 2.7.0 14 . For Chinese segmentation we used two different tools: KyTea 0.4.6 with Full SVM Model in MSR model and Stanford Word Segmenter version 2014-06-16 with Chinese Penn Treebank (CTB) and Peking University (PKU) model 15 (Tseng, 2005). For Korean segmentation we used mecabko 16 . For English and Indonesian segmentations we used tokenizer.perl 17 in the Moses toolkit. For Hindi segmentation we used Indic NLP Library 18 . Detailed procedures for the automatic evaluation are shown on the WAT2016 evaluation web page 19 . 4.2 Automatic Evaluation System The participants submit translation results via an automatic evaluation system deployed on the WAT2016 web page, which automatically gives evaluation scores for the uploaded results. Figure 1 shows the submission interface for participants. The system requires participants to provide the following information when they upload translation results: • Subtask: – Scientific papers subtask (J ↔ E, J ↔ C); – Patents subtask (C ↔ J, K ↔ J, E ↔ J); – Newswire subtask (I ↔ E) – Mixed domain subtask (H ↔ E, H ↔ J) • Method (SMT, RBMT, SMT and RBMT, EBMT, NMT, Other); 12 http://www.kecl.ntt.co.jp/icl/lirg/ribes/index.html 13 http://www.phontron.com/kytea/model.html 14 http://code.google.com/p/mecab/downloads/detail? name=mecab-ipadic-2.7.0-20070801.tar.gz 15 http://nlp.stanford.edu/software/segmenter.shtml 16 https://bitbucket.org/eunjeon/mecab-ko/ 17 https://github.com/moses-smt/mosesdecoder/tree/ RELEASE-2.1.1/scripts/tokenizer/tokenizer.perl 18 https://bitbucket.org/anoopk/indic nlp library 19 http://lotus.kuee.kyoto-u.ac.jp/WAT/evaluation/index.html 7
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: ASPEC JPC IITB BPPT pivot System ID
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
Page 67 and 68: Table 1: Automatic evaluation resul
Page 69 and 70: 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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