Improving Multi-turn Dialogue Modelling with Utterance ReWriter
This addresses the problem of understanding real intentions in multi-turn conversations for chatbots, but it is incremental as it builds on existing methods.
The paper tackles the challenge of coreference and information omission in multi-turn dialogue by proposing an utterance rewriter as a pre-processing step, which achieves remarkably good performance and brings general improvement across domains.
Recent research has made impressive progress in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challenge is the frequently occurred coreference and information omission in our daily conversation, making it hard for machines to understand the real intention. In this paper, we propose rewriting the human utterance as a pre-process to help multi-turn dialgoue modelling. Each utterance is first rewritten to recover all coreferred and omitted information. The next processing steps are then performed based on the rewritten utterance. To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network. We show the proposed architecture achieves remarkably good performance on the utterance rewriting task. The trained utterance rewriter can be easily integrated into online chatbots and brings general improvement over different domains.