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.

提出了一种名为 BiDeN 的通用对话编码器，用于捕捉对话的时间特征，将预训练语言模型用于编码对话记录是不够充分的，实验证明 BiDeN 在不同的下游任务中都表现出了卓越的效果。

回到未来：双向信息解耦网络用于多轮对话建模

Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling

In the retrieval-based multi-turn dialogue modeling, it remains a challenge
to select the most appropriate response according to extracting salient
features in context utterances. As a conversation goes on, topic shift at
discourse-level naturally happens through the continuous multi-turn dialogue
context. However, all known retrieval-based systems are satisfied with
exploiting local topic words for context utterance representation but fail to
capture such essential global topic-aware clues at discourse-level. Instead of
taking topic-agnostic n-gram utterance as processing unit for matching purpose
in existing systems, this paper presents a novel topic-aware solution for
multi-turn dialogue modeling, which segments and extracts topic-aware
utterances in an unsupervised way, so that the resulted model is capable of
capturing salient topic shift at discourse-level in need and thus effectively
track topic flow during multi-turn conversation. Our topic-aware modeling is
implemented by a newly proposed unsupervised topic-aware segmentation algorithm
and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each
topic segment with the response in a dual cross-attention way. Experimental
results on three public datasets show TADAM can outperform the state-of-the-art
method, especially by 3.3% on E-commerce dataset that has an obvious topic
shift.

本文提出了一种新颖的基于主题感知的无监督多轮对话建模方案，该方案通过无监督的主题感知分割算法和主题感知双交互匹配网络实现，在多个公共数据集上的实验结果表明 TADAM 可以胜过现有的最新方法，尤其是在出现明显主题转移的电子商务数据集上提高了 3.3％。