Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Extraction
Mi Zhang, Tieyun Qian, Ting Zhang, Xin Miao · 2023
The goal of relation extraction (RE) is to extract the semantic relations between/among entities in the text. As a fundamental task in information systems, it is crucial to ensure the robustness of RE models. Despite the high accuracy current deep neural models have achieved in RE tasks, they are easily affected by spurious correlations. One solution to this problem is to train the model with counterfactually augmented data (CAD) such that it can learn the causation rather than the confounding. However, no attempt has been made on generating counterfactuals for RE tasks.