Hybrid Simplification using Deep Semantics and Machine Translation

TLDR: A hybrid approach to sentence simplification which combines deep semantics and monolingual machine translation to derive simple sentences from complex ones that yields significantly simpler output that is both grammatical and meaning preserving.

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.