BAMGAN: a KBQA method based GAN and bi-directional attention

Knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. This method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN.