Simple and Effective Text Simplification Using Semantic and Neural Methods
This work addresses text simplification for natural language processing applications, presenting an incremental improvement over existing methods.
The paper tackled the problem of text simplification by introducing a semantic parser-based sentence splitting algorithm to improve neural machine translation's effectiveness, showing favorable results compared to state-of-the-art methods in combined lexical and structural simplification.
Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.