December 11-16 2016 Osaka Japan

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IITP English-Hindi Machine Translation System at WAT 2016 Sukanta Sen, Debajyoty Banik, Asif Ekbal, Pushpak Bhattacharyya Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar, India {sukanta.pcs15,debajyoty.pcs13,asif,pb}@iitp.ac.in Abstract In this paper we describe the system that we develop as part of our participation in WAT 2016. We develop a system based on hierarchical phrase-based SMT for English to Hindi language pair. We perform reordering and augment bilingual dictionary to improve the performance. As a baseline we use a phrase-based SMT model. The MT models are fine-tuned on the development set, and the best configurations are used to report the evaluation on the test set. Experiments show the BLEU of 13.71 on the benchmark test data. This is better compared to the official baseline BLEU score of 10.79. 1 Introduction In this paper, we describe the system that we develop as part of our participation in the Workshop on Asian Translation (WAT) 2016 (Nakazawa et al., 2016) for English-Hindi language pair. This year English-Hindi language pair is adopted for translation task for the first time in WAT. Apart from that, the said language pair was introduced in WMT 14 (Bojar et al., 2014). Our system is based on Statistical Machine Translation (SMT) approach. The shared task organizers provide English-Hindi parallel corpus for training and tuning and monolingual corpus for building language model. Literature shows that there exists many SMT based appraoches for differnt language pairs and domains. Linguistic-knowledge independent techniques such as phrase-based SMT (Koehn et al., 2003) and hierarchical phrase-based SMT (Chiang, 2005; Chiang, 2007) manage to perform efficiently as long as sufficient parallel text are available. Our submitted system is based on hierarchical SMT, performance of which is improved by performing reordering in the source side and augmenting English-Hindi bilingual dictionary. The rest of the paper is organized as follows. Section 2 describes the various methods that we use. Section 3 presents the details of datasets, experimental setup, results and analysis. Finally, Section 4 concludes the paper. 2 Method For WAT-2016, we have submitted two systems for English to Hindi (En-Hi) translation, viz. one without adding any external data to the training corpus and the other by augmenting bilingual dictionary in training. Both systems are reordered in the source side. As a baseline model we develop a phrase-based SMT model using Moses (Koehn et al., 2007). We perform several experiments with the hierarchical SMT in order to study the effectiveness of reordering and bilingual dictionary augmentation. These were done to improve syntactic order and alignment with linguistic knowledge. 2.1 Phrase-based Machine Translation Phrase-based statistical machine translation (PBSMT) (Koehn et al., 2003) is the most popular approach among all other approaches to machine translation and it has became benchmark for machine translation systems in academia as well as in industry. A phrase-based SMT consists 216 Proceedings of the 3rd Workshop on Asian Translation, pages 216–222, Osaka, Japan, December 11-17 2016.
Figure 1: Hierarchical approach with reordering and dictionary augmentation. of a language model, a translation model and a distortion model. Mathematically, it can be expressed as: e best = argmax e P (e|f) = argmax e [P (f|e)P LM (e)] (1) where, e best is the best translation, f is the source sentence, e is target sentence, P (f|e) and P LM (e) are translation model and language model respectively. P (f|e) (translation model) is further decomposed in phrase based SMT as, P ( ¯f 1 I |ē1 I ) = I∏ i=1 ϕ( ¯f i | ¯ e i )d(start i − end i−1 − 1) where, ϕ( ¯f i |ē i ) is, the probability that the phrase ¯f i is the translation of the phrase ē i , known as phrase translation probability which is learned from parallel corpus and d(start i − end i−1 − 1) is distortion probability which imposes an exponential cost on number of input words the decoder skips to generate next output phrase. Decoding process works by segmenting the input sentence f into sequence of I phrases ¯f 1 I distributed uniformly over all possible segmentations. Finally, it uses beam search to find the best translation. 2.2 Hierarchical Phrase based model Phrase-based model treats phrases as atomic units, where a phrase, unlike a linguistic phrase, is a sequence of tokens. These phrases are translated and reordered using a reordering model to produce an output sentence. This method can robustly translate the phrases that commonly occur in the training corpus. But authors (Koehn et al., 2003) have found that phrases longer than three words do not improve the performance much because data may be too sparse for learning longer phrases. Also, though the phrase-based approach is good at reordering of words but it fails at long distance phrase reordering using reordering model. To over come this, (Chiang, 2005) came up with Hierarchical PBSMT model which does not interfere with the strengths of the PBSMT instead capitalizes on them. Unlike the phrase-based SMT, it uses 217
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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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Figure 11: Official evaluation resu
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Figure 13: Official evaluation resu
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Annotator A Annotator B all weighte
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Kyoto-U 2 bjtu nlp UT-KAY 1 UT-KAY
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Online B IITP-MT EHR Online A ≫
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SYSTEM ID ID METHOD OTHER RESOURCES
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SYSTEM ID ID METHOD OTHER BLEU RIBE
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Hyoung-Gyu Lee, JaeSong Lee, Jun-Se
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Translation of Patent Sentences wit
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Chinese sentences contain fewer tha
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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
Page 179 and 180: Kenneth Heafield. 2011. Kenlm: Fast
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Page 185 and 186: 3.6 Ensembling Ensembling has previ
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Page 191 and 192: where W s ∈ R d×d is a matrix an
Page 193 and 194: Model BLEU (BP) RIBES Character-bas
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Page 203 and 204: 3 Results Team Other Resources Syst
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Page 207 and 208: Yonghui Wu, Mike Schuster, Zhifeng
Page 209 and 210: Language Sentence Chinese 该 / 钽
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Page 213 and 214: 4 Experiments and Results 4.1 Chine
Page 215 and 216: 6 Conclusion and Future Work In thi
Page 217 and 218: Controlling the Voice of a Sentence
Page 219 and 220: Figure 2: Flow of the voice predict
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Page 223 and 224: controlling the sentence length wit
Page 225 and 226: Chinese-to-Japanese Patent Machine
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Page 229: Conference on Natural Language Proc
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Page 235 and 236: Source: the rain and cold wind on W
Page 237 and 238: Residual Stacking of RNNs for Neura
Page 239 and 240: 2.2 Generalized Minimum Bayes Risk
Page 241 and 242: 5.0 4.5 4.0 Baseline model Enlarged
Page 243: Felix Hill, Kyunghyun Cho, Sebastie
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December 11-16 2016 Osaka Japan

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