Hybrid Simplification using Deep Semantics and Machine Translation

Shashi Narayan 1 Claire Gardent 1
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : We present a hybrid approach to sentence simplification which combines deep semantics and monolingual machine translation to derive simple sentences from complex ones. The approach differs from previous work in two main ways. First, it is semantic based in that it takes as input a deep semantic representation rather than e.g., a sentence or a parse tree. Second, it combines a simplification model for splitting and deletion with a monolingual translation model for phrase substitution and reordering. When compared against current state of the art methods, our model yields significantly simpler output that is both grammatical and meaning preserving.
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Communication dans un congrès
the 52nd Annual Meeting of the Association for Computational Linguistics, Jun 2014, Baltimore, United States. pp.435 - 445, 2014, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 〈http://aclweb.org/anthology//〉
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Shashi Narayan, Claire Gardent. Hybrid Simplification using Deep Semantics and Machine Translation. the 52nd Annual Meeting of the Association for Computational Linguistics, Jun 2014, Baltimore, United States. pp.435 - 445, 2014, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 〈http://aclweb.org/anthology//〉. 〈hal-01109581〉

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