December 11-16 2016 Osaka Japan

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The scores clearly indicate that both the approaches of LM i.e. neural LM generated from much bigger monolingual corpus or joint neural LM outperforms the baseline phrase-based SMT system. For WAT, the neural LM with word embedding dimensions of 700 units for the first hidden layer is submitted for participation. The BLEU score shows an improvement of 0.55 points over our baseline system. These scores may be improved with further tuning of the neural parameters. Some translation outputs of relatively better performing NPLM system compared against the reference sentences have been given in Table-3. An analysis of the translation output was done for NPLM based Indonesian-English MT system. The output sentences were adequate and fluent to some extent. The major error found was of dropping and insertion of phrases. In some instances, the Indonesian words could not be translated to English due to lack of vocabulary learnt. Though, OOV word percentage was found to be 5% of the total words in the test set. Another major pattern error was in the choice of function words used for English language. This error might require some linguistic insight on the Indonesian side of the language pair to understand the usage of function words in the source language. 4.2 English to Indonesian MT system For the reverse direction of language pair i.e. English-Indonesian, similar set of experiments were performed with same parameters as mentioned in section 4.1. The results obtained for the baseline phrase-based system, OSM based system, neural LM with additional monolingual data from commoncrawl with 3 different parameter variations and joint neural LM system have been given in Table-4. Since the authors do not know the Indonesian language, the translated output could not be manualy evaluated for error analysis at authors’ end. For this direction of language pair, the scores of OSM experiment is comaparable to baseline phrase based system with a score of 21.70 BLEU points. However, the joint neural language model has outperformed the neural LM and the baseline system by 0.61 BLEU scores. Joint neural LM output was submitted for manual evaluation. Approach Used BLEU score RIBES score AMFM score Phrase based SMT 21.74 0.804986 0.55095 Operation Sequence Model 21.70 0.806182 Neural LM with OE = 700 Neural LM with OE =750 Neural LM with OE = 800 Joint neural LM* 0.552480 22.12 0.804933 0.5528 21.64 0.806033 0.555 22.08 0.806697 0.55188 22.35 0.808943 0.55597 Table 4. Experiment Results for English-Indonesian MT system 4.3 Human Evaluation Result Analysis (OE – Output Embedding; * : submitted to WAT) As a part of shared task evaluation process, the translation system performance was human evaluated using two methods: pairwise crowdsourcing evaluation compared against the baseline system and JPO adequacy evaluation for content transmission. For Indonesian-English system, human evaluation was done on OSM system output. The crowdsourcing results show that 20% of the translations were better than the baseline system, 34% translations were comparable and 46% were worse than the baseline system. The system scored -26.00 in the crowdsourcing evaluation and 2.98 in adequacy evaluation. Table-5 shows the adequacy score 72
distribution as received in JPO adequacy evaluation. However, the automatic evaluation scores are found to be comparable to the baseline system. Experiment Indonesian- English Approach Followed OSM approach Adequacy distribution 5 4 3 2 1 Adequacy Score 12% 18.75% 31.75% 30.5% 7% 2.98 English- Indonesian NNJM 17.75% 25.25% 23.25% <strong>16</strong>.5% 17.25% 3.10 Table 5. JPO Adequacy scores for English Indonesian The joint neural LM approach for English-Indonesian system was submitted for human evaluation. The human evaluation scores shows that 23% of the translation were better than the baseline system, 44.75% were in tie with baseline system and 32.25% were worse than the baseline system. The crowdsourcing evaluation score is -9.250 and adequacy evaluation score is 3.10. For the JPO adequacy score, we observed that 33% sentences have at least 3 point difference between the annotator scores. The scores received have been given in Table-5. 5 Conclusion and future work In our research group, we have been working on a usecase related to English-Indonesian Machine Translation. This motivated us to participate in this shared task despite of having no exposure to Indonesian language. Since no member of the team had any previous experience with Indonesian language, not much of the linguistic insight was used in performing the experiments. This was an enriching experience in the terms of using computational ability for machine translation with minimum linguistic insight of one of the language in pair for translation. The BLEU scores show that using neural LM helps in improving the translation quality . In future , we would like to investigate the hyperparameters for the neural language model. We also plan to look at pure neural machine translation approaches for the English-Indoneian language pair. Reference Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. "Neural machine translation by jointly learning to align and translate." In ICLR. Cherry, Colin, and George Foster. 2012. "Batch tuning strategies for statistical machine translation." Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. Devlin, Jacob, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard M. Schwartz, and John Makhoul. 2014. "Fast and Robust Neural Network Joint Models for Statistical Machine Translation." In conference of the Association of Computational Linguistics. Durrani, Nadir, Helmut Schmid, and Alexander Fraser. 20<strong>11</strong>. "A joint sequence translation model with integrated reordering." Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics. Durrani, Nadir, Alexander M. Fraser, and Helmut Schmid. 2013. "Model With Minimal Translation Units, But Decode With Phrases." HLT-NAACL. 73
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WAT 2016 The 3rd Workshop on Asian
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Preface Many Asian countries are ra
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Technical Collaborators Luis Fernan
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Table of Contents Overview of the 3
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Conference Program December 12, 201
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December 12, 2016 (continued) 12:00
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Overview of the 3rd Workshop on Asi
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LangPair Train Dev DevTest Test JPC
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ASPEC JPC IITB BPPT pivot System ID
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3.6 Tree-to-String Syntax-based SMT
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• Use of other resources in addit
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ASPEC JPC BPPT IITBC pivot Team ID
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ut the adequacy is worse. As for AS
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Figure 3: Official evaluation resul
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Page 35 and 36: Figure 9: 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
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: dimensions of 150 units in second h
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
Page 113 and 114: 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.
Page 119 and 120: Figure 1: The framework of NMT. Whe
Page 121 and 122: For long sentence, we discarded all
Page 123 and 124: As shown in the above tables and fi
Page 125 and 126: Translation systems and experimenta
Page 127 and 128: The number of TM training sentence
Page 129 and 130: where, f, p and e means a source, p
Page 131 and 132: former system can translate more th
Page 133 and 134: Lexicons and Minimum Risk Training
Page 135 and 136: 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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