TAILIEUCHUNG - Báo cáo khoa học: "Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment"

This paper explores the use of bilingual parallel corpora as a source of lexical knowledge for cross-lingual textual entailment. We claim that, in spite of the inherent difficulties of the task, phrase tables extracted from parallel data allow to capture both lexical relations between single words, and contextual information useful for inference. | Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment Yashar Mehdad FBK-irst and Uni. of Trento Povo Trento Italy mehdad@ Matteo Negri FBK - irst Povo Trento Italy negri@ Marcello Federico FBK - irst Povo Trento Italy federico@ Abstract This paper explores the use of bilingual parallel corpora as a source of lexical knowledge for cross-lingual textual entailment. We claim that in spite of the inherent difficulties of the task phrase tables extracted from parallel data allow to capture both lexical relations between single words and contextual information useful for inference. We experiment with a phrasal matching method in order to i build a system portable across languages and ii evaluate the contribution of lexical knowledge in isolation without interaction with other inference mechanisms. Results 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 monolingual data. Finally we show that using parallel corpora to extract paraphrase tables reveals 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 inferred 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 current monolingual TE systems on lexical resources 1336 . WordNet VerbOcean FrameNet and deep processing components . syntactic and semantic parsers co-reference resolution tools temporal expressions recognizers and normalizers has to confront at the cross-lingual level with the limited .

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