December 11-16 2016 Osaka Japan

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IIT Bombay’s English-Indonesian submission at WAT: Integrating Neural Language Models with SMT Sandhya Singh Anoop Kunchukuttan Pushpak Bhattacharyya Center for Indian Language Technology Department of Computer Science & Engineering Indian Institute of Technology Bombay {sandhya, anoopk, pb}@cse.iitb.ac.in Abstract This paper describes the IIT Bombay’s submission as a part of the shared task in WAT 2016 for English–Indonesian language pair. The results reported here are for both the direction of the language pair. Among the various approaches experimented, Operation Sequence Model (OSM) and Neural Language Model have been submitted for WAT. The OSM approach integrates translation and reordering process resulting in relatively improved translation. Similarly the neural experiment integrates Neural Language Model with Statistical Machine Translation (SMT) as a feature for translation. The Neural Probabilistic Language Model (NPLM) gave relatively high BLEU points for Indonesian to English translation system while the Neural Network Joint Model (NNJM) performed better for English to Indonesian direction of translation system. The results indicate improvement over the baseline Phrase-based SMT by 0.61 BLEU points for English- Indonesian system and 0.55 BLEU points for Indonesian-English translation system. 1 Introduction This paper describes IIT Bombay’s submission for the English-Indonesian and Indonesian-English language pairs for the shared task in the 3rd Workshop on Asian Translation 1 (WAT) (Nakazawa et al., 2016). Every language pair in machine translation brings in new challenges in the form of their linguistic features. The Indonesian language, also known as Bahasa(Indonesia) is the official language of Indonesia. It is the fourth most populous country 2 in the world with approximately 190 million 3 people speaking this language. The language belongs to the Austronesian language family and has a lot of influence from Dutch language. It is also considered mutually intelligible with the Malay language. The script used is Roman/Latin script. The sentence structure followed is similar to English language i.e. Subject Verb Object (SVO). But it is highly agglutinative and morphologically rich as compared to English language. Hence, English-Indonesian is a very important language pair for translation studies. There is very limited work related to Indonesian language machine translation. Some of the previous work done is discussed here. Yulianti et al. (2011) experimented with a hybrid MT system (HMT) for Indonesian-English translation. They created a pipeline system where the input is first translated using a rule based MT system (RBMT) and the output is further processed with statistical MT system (SMT) to improve the translation quality. The results indicate that a pure SMT system outperforms HMT system in all cases. Larasati (2012) focused on resources and tool preparation for Indonesian-English SMT system as the author described this language pair as under-resourced and This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/ 2 http://www.infoplease.com/world/statistics/most-populous-countries.html 3 https://en.wikipedia.org/wiki/Indonesian_language 68 Proceedings of the 3rd Workshop on Asian Translation, pages 68–74, Osaka, Japan, December 11-17 2016.
under-studied. MorphInd, a morphanalyzer was developed as a part of the experiment. The tool could give more morphological information at a word level compared to its previous versions. The author also developed a standard parallel corpus IDENTIC, which could be used by the research community for MT related task. The experiment with preprocessed Indonesian data resulted in an improved SMT system output. Mantoro et al. (2013) attempted to find the optimal parameter for English-Indonesian SMT system by varying the weights of translation model, language model, distortion (reordering) and word penalty. And the optimally tuned SMT system is able to give a BLEU score of 22.14. Above discussed work clearly indicate that there is a lot of scope for experimentation for this language pair. Recently, Hermanto et al.(2015) performed an experimental study with RNN language model for English-Indonesian MT system. The experiment was done on a very small set of data for neural LM and the output was compared with SMT system trained on same data. The perplexity analysis of both the systems show that RNN model system outperforms SMT system with n-gram LM. The results of Hermanto et al.(2015) and various other research outcomes on different language pair using neural language model motivated our approach of experimentation using NLM and NNJM as a feature in SMT. 2 System Description For our participation in WAT 2016 shared task for English ßà Indonesian language pair, we experimented with the following systems – 1. Phrase-Based SMT system : This was our baseline system for the WMT shared task. The standard Moses Toolkit (Koehn et al., 2007) was used with MGIZA++ (Gao and Vogel, 2008) for word alignment on training corpus followed by grow-diag-final-and symmetrization heuristics for extracting phrases and lexicalized reordering. Tuning was done using Batch MIRA (Cherry and Foster, 2012) with the default 60 passes over the data and –return-best-dev flag to get the highest scoring run into the final moses.ini file. A 5-gram language model using SRILM toolkit (Stolcke, 2002) with Kneser-Ney smoothing was trained. 2. Use of Neural Language Model : A neural probabilistic language model was trained and integrated as a feature for the phrase-based translation model. For this, the default NPLM 4 implementation in Moses which is similar to the method described in Vaswani et al. (2013) was used. The goal was to examine if neural language models can improve the fluency for Indonesian-English translation and English-Indonesian translation by making use of distributed representations. We experimented with various word embedding sizes of 700, 750 and 800 for the first hidden layer in the network to get the optimal parameter while decoding. 3. Use of Bilingual Neural Joint Language Model : Devlin et al. (2014) have shown that including source side context information in the neural language model can lead to substantial improvement in translation quality. We experimented with Devlin's method which uses NPLM 3 in the back-end to train a neural network joint language model (NNJM) using parallel data and integrated it as a feature for the phrase-based translation as implemented in Moses. A 5-gram language model augmented with 9 source context words and single hidden layer required for fast decoding was used as a parameter to train the joint model. 4. Use of Operational Sequence Model : Operation sequence model was trained as it integrates N- gram-based reordering and translation in a single generative process which can result in relatively improved translation over phrase based system. OSM approach as suggested in Durrani et al. (2013) considers both source and target information for generating a translation. It deals with minimum translation units i.e. words, along with context information of source and target sentence which spans across phrasal boundries. A 5-gram OSM was used for the experimentation here. 4 http://nlg.isi.edu/software/nplm/ 69
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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 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: Kenneth Heafield. 2011. Kenlm: Fast
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
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
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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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