Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalizability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in \url{https://github.com/reml-group/ALO}.

翻译：问答方法以利用数据偏差而闻名，例如视觉问答中的语言先验和机器阅读理解（抽取式问答）中的位置偏差。当前的去偏差方法通常以显著降低内分布性能为代价来实现良好的外分布泛化能力，而非去偏差方法则以牺牲大量外分布性能为代价来获得高内分布性能。因此，它们难以应对复杂多变的现实世界情况。在本文中，我们提出了一种简单而有效的新型损失函数，具有自适应宽松优化，旨在为问答任务实现两者的最佳平衡。我们的主要技术贡献是根据小批量训练数据上前一优化状态与当前优化状态的比率自适应地减少损失。这种宽松优化可用于防止非去偏差方法过度学习数据偏差，同时使去偏差方法能够保持轻微的偏差学习。在视觉问答数据集（包括VQA v2、VQA-CP v1、VQA-CP v2、GQA-OOD）和抽取式问答数据集SQuAD上的实验表明，我们的方法使问答方法在大多数情况下获得了最先进的内分布和外分布性能。源代码已公开发布于\url{https://github.com/reml-group/ALO}。