Improving Background Based Conversation with Context-aware Knowledge Pre-selection
This work addresses the challenge of making dialogue systems more effective in leveraging background knowledge for users, though it is incremental as it builds on existing generation-based methods.
The paper tackled the problem of generation-based Background Based Conversations (BBCs) producing natural but ineffective responses by proposing a Context-aware Knowledge Pre-selection (CaKe) model, which improved informativeness and fluency and outperformed state-of-the-art baselines.
Background Based Conversations (BBCs) have been developed to make dialogue systems generate more informative and natural responses by leveraging background knowledge. Existing methods for BBCs can be grouped into two categories: extraction-based methods and generation-based methods. The former extract spans frombackground material as responses that are not necessarily natural. The latter generate responses thatare natural but not necessarily effective in leveraging background knowledge. In this paper, we focus on generation-based methods and propose a model, namely Context-aware Knowledge Pre-selection (CaKe), which introduces a pre-selection process that uses dynamic bi-directional attention to improve knowledge selection by using the utterance history context as prior information to select the most relevant background material. Experimental results show that our model is superior to current state-of-the-art baselines, indicating that it benefits from the pre-selection process, thus improving in-formativeness and fluency.