Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1336–1345, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment Yashar Mehdad FBK - irst and Uni. of Trento Povo (Trento), Italy mehdad@fbk.eu Matteo Negri FBK - irst Povo (Trento), Italy negri@fbk.eu Marcello Federico FBK - irst Povo (Trento), Italy federico@fbk.eu Abstract This paper explores the use of bilingual par- allel corpora as a source of lexical knowl- edge for cross-lingual textual entailment. We claim that, in spite of the inherent difficul- ties of the task, phrase tables extracted from parallel data allow to capture both lexical re- lations between single words, and contextual information useful for inference. We experi- ment with a phrasal matching method in or- der to: i) build a system portable across lan- guages, and ii) evaluate the contribution of lexical knowledge in isolation, without inter- action with other inference mechanisms. Re- sults achieved on an English-Spanish corpus obtained from the RTE3 dataset support our claim, with an overall accuracy above average scores reported by RTE participants on mono- lingual data. Finally, we show that using par- allel corpora to extract paraphrase tables re- veals their potential also in the monolingual setting, improving the results achieved with other sources of lexical knowledge. 1 Introduction Cross-lingual Textual Entailment (CLTE) has been proposed by (Mehdad et al., 2010) as an extension of Textual Entailment (Dagan and Glickman, 2004) that consists in deciding, given two texts T and H in different languages, if the meaning of H can be in- ferred from the meaning of T. The task is inherently difficult, as it adds issues related to the multilingual dimension to the complexity of semantic inference at the textual level. For instance, the reliance of cur- rent monolingual TE systems on lexical resources (e.g. WordNet, VerbOcean, FrameNet) and deep processing components (e.g. syntactic and semantic parsers, co-reference resolution tools, temporal ex- pressions recognizers and normalizers) has to con- front, at the cross-lingual level, with the limited availability of lexical/semantic resources covering multiple languages, the limited coverage of the ex- isting ones, and the burden of integrating language- specific components into the same cross-lingual ar- chitecture. As a first step to overcome these problems, (Mehdad et al., 2010) proposes a “basic solution”, that brings CLTE back to the monolingual scenario by translating H into the language of T. Despite the advantages in terms of modularity and portability of the architecture, and the promising experimental re- sults, this approach suffers from one main limitation which motivates the investigation on alternative so- lutions. Decoupling machine translation (MT) and TE, in fact, ties CLTE performance to the availabil- ity of MT components, and to the quality of the translations. As a consequence, on one side trans- lation errors propagate to the TE engine hampering the entailment decision process. On the other side such unpredictable errors reduce the possibility to control the behaviour of the engine, and devise ad- hoc solutions to …

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