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We present a new benchmark dataset called PARADE for paraphraseidentification that requires specialized domain knowledge. PARADE containsparaphrases that overlap very little at the lexical and syntactic level but aresemantically equivalent based on computer science domain knowledge, as well asnon-paraphrases that overlap greatly at the lexical and syntactic level but arenot semantically equivalent based on this domain knowledge. Experiments showthat both state-of-the-art neural models and non-expert human annotators havepoor performance on PARADE. For example, BERT after fine-tuning achieves an F1score of 0.709, which is much lower than its performance on other paraphraseidentification datasets. PARADE can serve as a resource for researchersinterested in testing models that incorporate domain knowledge. We make ourdata and code freely available.

PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge