Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling
Yiyang Li, Hai Yan Zhao, Zhuosheng Zhang · 2022
Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks.Recent studies of dialogue modeling commonly employ pre-trained language models (PrLMs) to encode the dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues.Therefore, we propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder, which explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks.Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.The official implementation of BiDeN is available at https://github.com/ EricLee8/BiDeN.