Hybrid Simplification using Deep Semantics and Machine Translation

Shashi Narayan, Claire Gardent

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationBaltimore, Maryland
PublisherAssociation for Computational Linguistics
Pages435-445
Number of pages11
Publication statusPublished - 1 Jun 2014

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