2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | HLT-11.3 | ||
| Paper Title | CONVERSATIONAL QUERY REWRITING WITH SELF-SUPERVISED LEARNING | ||
| Authors | Hang Liu, Meng Chen, Youzheng Wu, Xiaodong He, Bowen Zhou, JD AI, China | ||
| Session | HLT-11: Language Understanding 3: Speech Understanding - General Topics | ||
| Location | Gather.Town | ||
| Session Time: | Thursday, 10 June, 13:00 - 13:45 | ||
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 | ||
| Presentation | Poster | ||
| Topic | Human Language Technology: [HLT-DIAL] Discourse and Dialog | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Context modeling plays a critical role in building multi-turn dialogue systems. Conversational Query Rewriting (CQR) aims to simplify the multi-turn dialogue modeling into a single-turn problem by explicitly rewriting the conversational query into a self-contained utterance. However, existing approaches rely on massive supervised training data, which is labor-intensive to annotate. And the detection of the omitted important information from context can be further improved. Besides, intent consistency constraint between contextual query and rewritten query is also ignored. To tackle these issues, we first propose to construct a large-scale CQR dataset automatically via self-supervised learning, which does not need human annotation. Then we introduce a novel CQR model Teresa based on Transformer, which is enhanced by self-attentive keywords detection and intent consistency constraint. Finally, we conduct extensive experiments on two public datasets. Experimental results demonstrate that our proposed model outperforms existing CQR baselines significantly, and also prove the effectiveness of self-supervised learning in improving the CQR performance. | ||