Work

Simple and Effective Text Simplification Using Semantic and Neural Methods

Year: 2018

Type: conference-paper

Abstract: 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 overconservative, 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 stateof-the-art in combined lexical and structural simplification.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p18-1016

Authors: Elior Sulem, Omri Abend, Ari Rappoport

Institution: Hebrew University of Jerusalem

Language: English

Topic: Text Readability and Simplification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Hebrew University of Jerusalem, Israel Science Foundation, Intel Collaboration Research Institute for Computational Intelligence

Award: G5348532491