Neural Sentence Simplification with Semantic Dependency Information

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.

Authors

• Zhe Lin Adobe Research
• Xiaojun Wan Peking University

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