Neural Sentence Simplification with Semantic Dependency Information

Authors

  • Zhe Lin Wangxuan Institute of Computer Technology, Peking University Center for Data Science, Peking University The MOE Key Laboratory of Computational Linguistics, Peking University
  • Xiaojun Wan Wangxuan Institute of Computer Technology, Peking University Center for Data Science, Peking University The MOE Key Laboratory of Computational Linguistics, Peking University

Keywords:

Generation, Applications

Abstract

Most previous works on neural sentence simplification exploit seq2seq model to rewrite a sentence without explicitly considering the semantic information of the sentence. This may lead to the semantic deviation of the simplified sentence. In this paper, we leverage semantic dependency graph to aid neural sentence simplification system. We propose a new sentence simplification model with semantic dependency information, called SDISS (as shorthand for Semantic Dependency Information guided Sentence Simplification), which incorporates semantic dependency graph to guide sentence simplification. We evaluate SDISS on three benchmark datasets and it outperforms a number of strong baseline models on the SARI and FKGL metrics. Human evaluation also shows SDISS can produce simplified sentences with better quality.

Downloads

Published

2021-05-18

How to Cite

Lin, Z., & Wan, X. (2021). Neural Sentence Simplification with Semantic Dependency Information. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15), 13371-13379. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17578

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing II