Generating Consistent and Diverse QA pairs from Contexts with BN Conditional VAE
Jin Li, Peng Qi, Hong Luo · 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) · 2022
One of the most challenging problems in the question answering (QA) area is the lack of high-quality labeled data. However, the cost of manually labeling a question-answer (QA) pair from the target text is very high. One way to solve this problem is to automatically generate QA pairs from the target text. In this paper, we propose the Batch Normalization conditional VAE-QA pair generation (BNCVAE-QAG) model to generate QA pairs for a given text. First, we employ the Batch Normalization (BN) layer to prevent Kullback-Leibler (KL) divergence from disappearing. We also design modules to extract spatiotemporal features from text contents, in addition, the self-attention mechanism is employed in the question decoder, which improves the accuracy and recall of results. Furthermore, we propose a question generalization mechanism to generate more QA pairs. We evaluate our BNCVAE-QAG model on several datasets. The experimental results show that our model has achieved an impressive performance improvement than the baseline.