Neural Sentence Simplification with Semantic Dependency Information
DOI:
https://doi.org/10.1609/aaai.v35i15.17578Keywords:
Generation, ApplicationsAbstract
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. https://doi.org/10.1609/aaai.v35i15.17578
Issue
Section
AAAI Technical Track on Speech and Natural Language Processing II