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LexDivPara: A Measure of Paraphrase Quality with Integrated Sentential Lexical Complexity

https://doi.org/10.1007/978-3-030-82199-9_1

Tanh, T. ; Do, Ha ; Tanh, D. ; Shi, Pu ; Sathyanarayanan, A. ; Khan, S. ; Kohei, A. ( August 2021 , International Workshop on Intelligent Systems and Applications)

We present a novel method that automatically measures quality of sentential paraphrasing. Our method balances two conflicting criteria: semantic similarity and lexical diversity. Using a diverse annotated corpus, we built learning to rank models on edit distance, BLEU, ROUGE, and cosine similarity features. Extrinsic evaluation on STS Benchmark and ParaBank Evaluation datasets resulted in a model ensemble with moderate to high quality. We applied our method on both small benchmarking and large-scale datasets as resources for the community.
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