Abstract
The word level system combination, which is better than phrase level and sentence level, has emerged as a powerful post-processing method for statistical machine translation (SMT). This paper first give the definition of HyperGraph(HG) as a kind of compact data structure in SMT, and then introduce simple bracket transduction grammar(SBTG) for hypergraph decoding. To optimize the more feature weights, we introduce minimum risk (MR) with deterministic annealing (DA) into the training criterion, and compare two classic training procedures in experiment. The deoding approaches of n-gram model based on hypergraph are shown to be superior to conventional cube pruning in the setting of the Chinese-to-English track of the 2008 NIST Open MT evaluation.
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References
Menezes, A., Quirk, C.: Using Dependency Order Templates to Improve Generality in Translation. In: Proc. 2nd WMT at ACL, Prague, Czech Republic (2007)
Rosti, A.-V.I., Matsoukas, S., Schwartz, R.: Improved Word-level System Combination for Machine Translation. In: Proceedings of ACL (2007)
Wang, C., Collins, M., Koehn, P.: Chinese Syntactic Reordering for Statistical Machine Translation. In: EMNLP 2007 (2007)
Li, C.-H., Zhang, D., Li, M., Zhou, M., Li, C.-H., Li, M., Guan, Y.: A Probabilistic Approach to Syntax-based Reordering for Statistical Machine Translation. In: Proceedings of ACL (2007)
Li, C.-H., He, X., Liu, Y., Xi, N.: Incremental HMM Alignment for MT System Combination. In: Proceedings of ACL (2009)
Karakos, D., Eisner, J., Khudanpur, S., Dreyer, M.: Machine Translation System Combination using ITG-based Alignments. In: Proceedings of ACL (2008)
Chiang, D.: Hierarchical Phrase-based Translation. Computational Linguistics 33(2) (2007)
Xiong, D., Liu, Q., Lin, S.: Maximum Entropy based Phrase Reordering Model for Statistical Machine Translation. In: Proceedings of COLING/ACL 2006, Sydney, Australia, pp. 521–528 (July 2006)
Matusov, E., Ueffing, N., Ney, H.: Computing Consensus Translation from Multiple Machine Translation Systems using Enhanced Hypothesis Alignment. In: Proceedings of EACL (2006)
Feng, Y., Liu, Y., Mi, H., Liu, Q., Lu, Y.: Lattice-based System Combination for Statistical Machine Translation. In: Proceedings of ACL (2009)
Foster, G., Kuhn, R.: Mixture-Model Adaptation for SMT. In: Proc. of the Second ACL Workshop on Statistical Machine Translation, pp. 128–136 (2007)
Denero, J., Chiang, D., Knight, K.: Fast Consensus Decoding over Translation Forest. In: Proceedings of ACL (2009)
DeNero, J., Kumar, S., Chelba, C., Och, F.: Model Combination for Machine Translation. In: Proceedings of NAACL (2010)
Sim, K.C., Byrne, W.J., Gales, M.J.F., Sahbi, H., Woodland, P.C.: Consensus Network Decoding for Statistical Machine Translation System Combination. In: Proc. of ICASSP, pp. 105–108 (2007)
Levy, R., Manning, C.: Is It Harder To Parse Chinese, or The Chinese Treebank? Published in Proceedings of ACL 2003 (2003)
Huang, L., Chiang, D.: Better k-best Parsing. In: Proceedings of the International Workshop on Parsing Technologies (IWPT), pp. 53–64 (2005)
Huang, L., Chiang, D.: Forest Rescoring: Faster Decoding with Integrated Language Models. In: Proceedings of ACL, Prague, Czech Rep. (2007)
Huang, L.: Forest reranking: Discriminative parsing with Non-local Features. In: Proc. of (2008)
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A Study of Translation Edit Rate with Targeted Human Annotation. In: Proceedings of AMTA (2006)
Moore, R., Quirk, C.: Faster Beam-Search Decoding for Phrasal Statistical Machine Translation. In: Proc. of MT Summit XI (2007)
Kumar, S., Macherey, W., Dyer, C., Och, F.: Efficient Minimum Error Rate Training and Minimum Bayes-Risk Decoding for Translation Hypergraphs and Lattices. In: Proceedings of ACL, pp. 163–171 (2009)
Bangalore, S., Bordel, G., Riccardi, G.: Computing Consensus Translation from Multiple Machine Translation Systems. In: Workshop on Automatic Speech Recognition and Understanding, Madonna di Campiglio, Italy, pp. 351–354 (2001)
He, X., Yang, M., Gao, J., Nguyen, P., Moore, R.: Indirect-HMM based Hypothesis Alignment for Combining Outputs from Machine Translation Systems. In: Proc. of EMNLP (2008)
He, X., Toutanova, K.: Joint Optimization for Machine Translation System Combination. In: Proc. of EMNLP (2009)
Liu, Y., Mi, H., Feng, Y., Liu, Q.: Joint Decoding with Multiple Translation Models. In: Proc. of ACL, pp. 576–584 (2009)
Li, Z., Eisner, J., Khudanpur, S.: Variational Decoding for Statistical Machine Translation. In: Proceedings of ACL (2009a)
Li, Z., Eisner, J.: First- and Second-order Expectation Semirings with Applications to Minimum-Risk Training on Translation Forests. In: Proceedings of EMNLP (2009b)
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Liu, Y., Li, S., Zhao, T. (2011). A Decoding Method of System Combination Based on Hypergraph in SMT. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_14
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DOI: https://doi.org/10.1007/978-3-642-23896-3_14
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