UnitedQA: A Hybrid Approach for Open Domain Question Answering

Understand

To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively.

• In this paper, we study a hybrid approach for leveraging the strengths of both models.
• We apply novel techniques to enhance both extractive and generative readers built upon recent pretrained neural language models, and find that proper training methods can provide large improvement over previous state-of-the-art models.
• We demonstrate that a simple hybrid approach by combining answers from both readers can efficiently take advantages of extractive and generative answer inference strategies and outperforms single models as well as homogeneous ensembles.