SWING: Weakly Supervised Table Question Answering with Self-training via Reinforcement Learning

Previous works with auto-regressive pre-trained language model achieved state-of-art results in semantic parsing tasks. On the other hand, these models generate answers directly, it is difficult to get selected cells for generated answers and answers that require numerical reasoning. In this paper, we present a self-training framework to generate logical forms with weakly supervised question answering. Self-training framework composed of the training procedure and pseudo label correction procedure. In the training procedure, we train models to generate column and row positions of answer cells with supervised training data whose tagged answer cells are a subset of the input table cells. In the pseudo label correction procedure, we correct the tagged dataset for training the model. Furthermore, to enhance the correcting accuracy, we employed reinforcement learning, incorporating a reward function that yields a greater reward as the computed correct answer approaches the benchmark correct answer. Ultimately, we observed an improvement in accuracy from 47.1 to 54.06, a 6.89 increase, using the tapex-base model. Furthermore, we managed to outperform the existing performance, achieving 64.17, using the OmniTab model. Thus, our proposed method, SWING, a self-learning table parsing framework, has shown improved performance in numerical reasoning cases, and can predict rationale cells that allow auto-regressive PLM to compute answers without the need for logical form.