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.

翻译：作为具有挑战性的自然语言理解(NLU)分支的多方向对话模式,旨在为机器建立理解人类对话的代表机构,为多重下游任务奠定坚实的基础,最近关于对话模式的研究通常使用预先培训的语言模式(PrLMs),将对话历史编码为连续代号,这不足以捕捉对话的时间特征。因此,我们提议双向信息脱钩网络(BiDeN)作为一个普遍对话编码器,明确纳入过去和今后的情况,并可以推广到与对话有关的广泛任务中。 不同下游任务数据集的实验结果显示了我们双向信息脱钩网络的普遍性和有效性。