Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology
Son Quoc Tran, Matt Kretchmar · 2024
This paper proposes a novel training method to improve the robustness of Extractive Question Answering (EQA) models.Previous research has shown that existing models, when trained on EQA datasets that include unanswerable questions, demonstrate a significant lack of robustness against distribution shifts and adversarial attacks.Despite this, the inclusion of unanswerable questions in EQA training datasets is essential for ensuring real-world reliability.Our proposed training method includes a novel loss function for the EQA problem and challenges an implicit assumption present in numerous EQA datasets.Models trained with our method maintain in-domain performance while achieving a notable improvement on outof-domain datasets.This results in an overall F1 score improvement of 5.7 across all testing sets.Furthermore, our models exhibit significantly enhanced robustness against two types of adversarial attacks, with a performance decrease of only about one-third compared to the default models. 1