Abstract
We present a linguistically-motivated sub-sentential alignment system that extends the intersected IBM Model 4 word alignments. The alignment system is chunk-driven and requires only shallow linguistic processing tools for the source and the target languages, i.e. part-of-speech taggers and chunkers.
We conceive the sub-sentential aligner as a cascaded model consisting of two phases. In the first phase, anchor chunks are linked based on the intersected word alignments and syntactic similarity. In the second phase, we use a bootstrapping approach to extract more complex translation patterns.
The results show an overall AER reduction and competitive F-Measures in comparison to the commonly used symmetrized IBM Model 4 predictions (intersection, union and grow-diag-final) on six different text types for English-Dutch. More in particular, in comparison with the intersected word alignments, the proposed method improves recall, without sacrificing precision. Moreover, the system is able to align discontiguous chunks, which frequently occur in Dutch.
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References
Planas, E.: SIMILIS Second-generation translation memory software. In: 27th International Conference on Translating and the Computer (TC27), London, United Kingdom, ASLIB (2005)
Itagaki, M., Aikawa, T., He, X.: Automatic Validation of Terminology Consistency with Statistical Method. In: Machine Translation Summit XI. European Associaton for Machine Translation, pp. 269–274 (2007)
Macken, L., Lefever, E., Hoste, V.: Linguistically-based Sub-sentential Alignment for Terminology Extraction from a Bilingual Automotive Corpus. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, United Kingdom (2008)
Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29(1), 19–51 (2003)
Brown, P.F., Della Pietra, V.J., Della Pietra, S.A., Mercer, R.L.: The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics 19(2), 263–311 (1993)
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E.: Moses: Open Source Toolkit for Statistical Machine Translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, Czech Republic, Prague. Association for Computational Linguistics, pp. 177–180 (2007)
Ganchev, K., Graça, J.V., Taskar, B.: Better Alignments = Better Translations? In: Proceedings of ACL 2008: HLT, Columbus, Ohio. Association for Computational Linguistics, pp. 986–993 (2008)
Zhang, H., Quirk, C., Moore, R.C., Gildea, D.: Bayesian Learning of Non-Compositional Phrases with Synchronous Parsing. In: Proceedings of ACL 2008: HLT, Columbus, Ohio. Association for Computational Linguistics, pp. 97–105 (2008)
DeNero, J., Klein, D.: Tailoring Word Alignments to Syntactic Machine Translation. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic. Association for Computational Linguistics, pp. 17–24 (2007)
Tiedemann, J.: Combining Clues for Word Alignment. In: Proceedings of the 10th Conference of the European Chapter of the ACL (EACL 2003), Budapest, Hungary (2003)
Daelemans, W., van den Bosch, A.: Memory-based language processing. Cambridge University Press, Cambridge (2005)
van den Bosch, A., Busser, B., Daelemans, W., Canisius, S.: An efficient memory-based morphosyntactic tagger and parser for Dutch. In: Selected Papers of the 17th Computational Linguistics in the Netherlands Meeting, Leuven, Belgium, pp. 191–206 (2007)
Abney, S.: Parsing by chunks. In: Berwick, R., Abney, S., Tenny, C. (eds.) Principle-Based Parsing. Kluwer Academic Publisher, Dordrecht (1991)
Melamed, D.I.: Models of translational equivalence among words. Computational Linguistics 26(2), 221–249 (2000)
Moore, R.C.: Association-Based Bilingual Word Alignment. In: ACL Workshop on Building and Using Parallel Texts, Ann Arbor, Michigan, United States, pp. 1–8 (2005)
Dunning, T.: Accurate Methods for the Statistics of Surprise and Coincidence. Computational Linguistics 19(1), 61–74 (1993)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. Massachusetts Institute of Technology (2003)
McEnery, T., Xiao, R., Yukio, T.: Corpus-based Language Studies. An advanced resource book. Routledge, London (2006)
Macken, L., Trushkina, J., Rura, L.: Dutch Parallel Corpus: MT corpus and Translator’s Aid. In: Machine Translation Summit XI, Copenhagen, Denmark, pp. 313–320 (2007)
Melamed, D.I.: Empirical Methods for Exploiting Parallel Texts. MIT Press, Cambridge (2001)
Davis, P.C.: Stone Soup Translation: The Linked Automata Model, Unpublished PhD, Ohio State University (2002)
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Macken, L., Daelemans, W. (2010). A Chunk-Driven Bootstrapping Approach to Extracting Translation Patterns. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2010. Lecture Notes in Computer Science, vol 6008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12116-6_33
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