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Robustness to Modification with Shared Words in Paraphrase Identification
2020
Findings of the Association for Computational Linguistics: EMNLP 2020
unpublished
Revealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations. In this paper, we study the robustness of paraphrase identification models from a new perspective -via modification with shared words, and we show that the models have significant robustness issues when facing such modifications. To modify an example consisting of a sentence pair, we either replace some words shared by both sentences
doi:10.18653/v1/2020.findings-emnlp.16
fatcat:h6udubsl4rgwtpueutnvcerohi