1. Paper title
Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
2. link
https://www.aclweb.org/anthology/2020.acl-main.20.pdf
3. 摘要
One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer (QA) pairs for a target text domain with human annotation. An alternative approach to tackle the problem is to use automatically generated QA pairs from either the problem context or from large amount of unstructured texts (e.g. Wikipedia). In this work, we propose a hierarchical conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QA pairs to ensure their consistency. We validate our Information Maximizing Hierarchical Conditional Variational AutoEncoder (InfoHCVAE) on several benchmark datasets by evaluating the performance of the QA model (BERT-base) using only the generated QA pairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semisupervised learning) for training, against stateof-the-art baseline models. The results show that our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training. 1
4. 要解决什么问题
QA问题缺少labelled data，需要根据文本自动生成问题对。
5. 作者的主要贡献
- 用hierarchical conditional variational autoencoder (HCVAE)生成QA对
- 确保生成的QA对的一致性。
6. 得到了什么结果
同时使用生成QA对和真实QA对训练得到的性能优于只使用真实QA对训练的性能。
7. 关键字
生成QA对