# QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization Source: [QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:46:30.691Z Authors: Yi-Ting Yeh, Yun-Nung Chen Published: 2019-08-31T13:50:28.000Z ## Abstract Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems truly understand language remains unknown, and existing systems are not good at distinguishing distractor sentences, which look related but do not actually answer the question. To address this problem, we propose QAInfomax as a regularizer in reading comprehension systems by maximizing mutual information among passages, a question, and its answer. QAInfomax helps regularize the model to not simply learn the superficial correlation for answering questions. The experiments show that our proposed QAInfomax achieves the state-of-the-art performance on the benchmark Adversarial-SQuAD dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()