Abstract
Existing sentence alignment methods are founded fundamentally on sentence length and lexical correspondences. Methods based on the former follow in general the length proportionality assumption that the lengths of sentences in one language tend to be proportional to that of their translations, and are known to bear poor adaptivity to new languages and corpora. In this paper, we attempt to interpret this assumption from a new perspective via the notion of collaborative matching, based on the observation that sentences can work collaboratively during alignment rather than separately as in previous studies. Our approach is tended to be independent on any specific language and corpus, so that it can be adaptively applied to a variety of texts without binding to any prior knowledge about the texts. We use one-to-one sentence alignment to illustrate this approach and implement two specific alignment methods, which are evaluated on six bilingual corpora of different languages and domains. Experimental results confirm the effectiveness of this collaborative matching approach.
The paper was supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (No. 2017ZT07X355).
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Quan, X., Kit, C., Chen, W. (2018). Collaborative Matching for Sentence Alignment. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_4
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DOI: https://doi.org/10.1007/978-3-030-01716-3_4
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