Sentence Simplification as Tree Transduction

In this paper, we introduce a syntax-based sentence simplifier that models simplifi-cation using a probabilistic synchronous tree substitution grammar (STSG). To im-prove the STSG model specificity we uti-lize a multi-level backoff model with addi-tional syntactic annotations that allow for better discrimination over previous STSG formulations. We compare our approach to T3 (Cohn and Lapata, 2009), a re-cent STSG implementation, as well as two state-of-the-art phrase-based sentence simplifiers on a corpus of aligned sen-tences from English and Simple English Wikipedia. Our new approach performs significantly better than T3, similarly to human simplifications for both simplicity and fluency, and better than the phrase-based simplifiers for most of the evalua-tion metrics.