Incorporating target language semantic roles into a string-to-tree translation model

Chao SU; Yu-hang GUO; He-yan HUANG; Shu-min SHI; Chong FENG

doi:10.1631/FITEE.1601349

Front. Inform. Technol. Electron. Eng ›› 2017, Vol. 18 ›› Issue (10) : 1534 -1542. DOI: 10.1631/FITEE.1601349

Article

Incorporating target language semantic roles into a string-to-tree translation model

Chao SU ¹^,³^,^†
, Yu-hang GUO ¹
, He-yan HUANG ¹^,²^,^†
, Shu-min SHI ¹^,²
, Chong FENG ¹^,²

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Abstract

The string-to-tree model is one of the most successful syntax-based statistical machine translation (SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model: (1) adding role labels in the syntax tree; (2) constructing a semantic role tree, and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora, and the two proposed methods surpass the phrase-based system on large-scale training data.

Keywords

Machine translation / Semantic role / Syntax tree / String-to-tree

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Chao SU, Yu-hang GUO, He-yan HUANG, Shu-min SHI, Chong FENG. Incorporating target language semantic roles into a string-to-tree translation model. Front. Inform. Technol. Electron. Eng, 2017, 18(10): 1534-1542 DOI:10.1631/FITEE.1601349

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References

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[1]	Aziz, W., Rios, M., Specia, L., 2011. Shallow semantic trees for SMT. Proc. 6th Workshop on Statistical Machine Translation, p.316–322.

[2]	Baker, C.F., Fillmore, C.J., Lowe, J.B., 1998. The Berkeley Framenet Project. Proc. 17th Int. Conf. on Computational Linguistics, p.86–90.

[3]	Bazrafshan, M., Gildea, D., 2013. Semantic roles for string to tree machine translation. Proc. 51st Annual Meeting of the Association for Computational Linguistics, p.419–423.

[4]	Brown, P.F., Cocke, J., Pietra, S.A.D., , 1990. A statistical approach to machine translation. Comput. Ling., 16(2): 79–85.

[5]	Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., , 1993. The mathematics of statistical machine translation: parameter estimation. Comput. Ling., 19(2):263–311.

[6]	Chiang, D., 2005. A hierarchical phrase-based model for statistical machine translation. Proc. 43rd Annual Meeting on Association for Computational Linguistics, p.263–270.

[7]	Clark, H.J., Dyer, C., Lavie, A., , 2011. Better hypothesis testing for statistical machine translation: controlling for optimizer instability. Proc. 49th Annual Meeting of the Association for Computational Linguistics, p.176–181.

[8]	Denkowski, M., Lavie, A., 2014. Meteor universal: language specific translation evaluation for any target language. Proc. 9th Workshop on Statistical Machine Translation, p.376–380.

[9]	Galley, M., Hopkins, M., Knight, K., , 2004. What’s in a translation rule. Proc. Human Language Technology Conf. of the North American Chapter of the Association for Computational Linguistics.

[10]	Gildea, D., Jurafsky, D., 2002. Automatic labeling of semantic roles. Comput. Ling., 28(3):245–288.

[11]	Huang, L., Chiang, D., 2005. Better k-best parsing. Proc. 9th Int. Workshop on Parsing Technology, p.53–64.

[12]	Koehn, P., 2004. Statistical significance tests for machine translation evaluation. Proc. Conf. on Empirical Methods in Natural Language Processing, p.388–395.

[13]	Koehn, P., Och, F.J., Marcu, D., 2003. Statistical phrase-based translation. Proc. Conf. of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.48–54.

[14]	Koehn, P., Hoang, H., Birch, A., , 2007. Moses: open source toolkit for statistical machine translation. Proc. 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, p.177–180.

[15]	Komachi, M., Matsumoto, Y., Nagata, M., 2006. Phrase reordering for statistical machine translation based on predicate-argument structure. Int. Workshop on Spoken Language Translation, p.77–82.

[16]	Liu, D., Gildea, D., 2008. Improved tree-to-string transducer for machine translation. Proc. 3rd Workshop on Statistical Machine Translation, p.62–69.

[17]	Liu, D., Gildea, D., 2010. Semantic role features for machine translation. Proc. 23rd Int. Conf. on Computational Linguistics, p.716–724.

[18]	Liu, Y., Liu, Q., 2010. Joint parsing and translation. Proc. 23rd Int. Conf. on Computational Linguistics, p.707–715.

[19]	Liu, Y., Liu, Q., Lin, S., 2006. Tree-to-string alignment template for statistical machine translation. Proc. 21st Int. Conf. on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, p.609–616.

[20]	Marcu, D., Wang, W., Echihabi, A., , 2006. SPMT: Statistical machine translation with syntactified target language phrases. Proc. Conf. on Empirical Methods in Natural Language Processing, p.44–52.

[21]	Meyers, A., Reeves, R., Macleod, C., , 2004. The nombank project: an interim report. HLT-NAACL Workshop: Frontiers in Corpus Annotation, p.24–31.

[22]	Mi, H., Huang, L., Liu, Q., 2008. Forest-based translation. Proc. ACL-08: HLT, p.192–199.

[23]	Och, F.J., Ney, H., 2004. The alignment template approach to statistical machine translation. Comp. Ling., 30(4):417–449.

[24]	Palmer, M., Gildea, D., Kingsbury, P., 2005. The proposition bank: an annotated corpus of semantic roles. Comp. Ling., 31(1):71–106.

[25]	Papineni, K., Roukos, S., Ward, T., , 2002. BLEU: a method for automatic evaluation of machine translation. Proc. 40th Annual Meeting on Association for Computational Linguistics, p.311–318.

[26]	Petrov, S., Barrett, L., Thibaux, R., , 2006. Learning accurate, compact, and interpretable tree annotation. Proc. 21st Int. Conf. on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, p.433–440.

[27]	Pradhan, S.S., Ward, W., Hacioglu, K., , 2004. Shallow semantic parsing using support vector machines. Human Language Technologies: the Annual Conf. of the North American Chapter of the Association for Computational Linguistics, p.233–240.

[28]	Wu, D., 1995. Grammarless extraction of phrasal translation examples from parallel texts. Proc. 6th Int. Conf. on Theoretical and Methodological Issues in Machine Translation, p.354–372.

[29]	Wu, D., 1996. A polynomial-time algorithm for statistical machine translation. Proc. 34th Annual Meeting on Association for Computational Linguistics, p.152–158.

[30]	Wu, D., Fung, P., 2009. Semantic roles for SMT: a hybrid two-pass model. Proc. Human Language Technologies: the Annual Conf. North American Chapter of the Association for Computational Linguistics, p.13–16.

[31]	Xiong, D., Zhang, M., Li, H., 2012. Modeling the translation of predicate-argument structure for SMT. Proc. 50th Annual Meeting of the Association for Computational Linguistics, p.902–911.

[32]	Yamada, K., Knight, K., 2001. A syntax-based statistical translation model. Proc. 39th Annual Meeting on Association for Computational Linguistics, p.523–530.

[33]	Zhai, F., Zhang, J., Zhou, Y., , 2012. Machine translation by modeling predicate-argument structure transformation. Proc. Int. Conf. on Computational Linguistics, p.3019–3036.