Exploiting Qualitative Information from Automatic Word Alignment for Cross-lingual NLP Tasks

Jose ́ Guilherme Camargo de Souza, Miquel Esplà-Gomis, Marco Turchi and Matteo Negri

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

The use of automatic word alignment to capture sentence-level semantic relations is common to a number of cross-lingual NLP applications. Despite its proved usefulness, however, word alignment information is typically considered from a quantitative point of view (e.g. the number of alignments), disregarding qualitative aspects (the importance of aligned terms). In this paper we demonstrate that integrating qualitative information can bring significant performance improvements with negligible impact on system complexity. Focusing on the cross-lingual textual entailment task, we contribute with a novel method that: i) significantly outperforms the state of the art, and ii) is portable, with limited loss in performance, to language pairs where training data are not available.

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