Robustness to Modification with Shared Words in Paraphrase Identification
This addresses robustness issues in NLP models for paraphrase identification, which is incremental as it introduces a new perspective on modifications.
The paper tackles the robustness of paraphrase identification models by modifying sentence pairs with shared words, showing that models' performance dramatically drops on these modified examples, and that adversarial training can mitigate the issue.
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 or introduce new shared words. We aim to construct a valid new example such that a target model makes a wrong prediction. To find a modification solution, we use beam search constrained by heuristic rules, and we leverage a BERT masked language model for generating substitution words compatible with the context. Experiments show that the performance of the target models has a dramatic drop on the modified examples, thereby revealing the robustness issue. We also show that adversarial training can mitigate this issue.