Lexical Simplification with the Deep Structured Similarity Model
نویسندگان
چکیده
We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the stateof-the-art on two standard datasets used for the task.
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