While participants in a multi-party multi-turn conversation simultaneously
engage in multiple conversation topics, existing response selection methods are
developed mainly focusing on a two-party single-conversation scenario. Hence,
the prolongation and transition of conversation topics are ignored by current
methods. In this work, we frame response selection as a dynamic topic tracking
task to match the topic between the response and relevant conversation context.
With this new formulation, we propose a novel multi-task learning framework
that supports efficient encoding through large pretrained models with only two
utterances at once to perform dynamic topic disentanglement and response
selection. We also propose Topic-BERT an essential pretraining step to embed
topic information into BERT with self-supervised learning. Experimental results
on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response
selection and topic disentanglement tasks outperforming existing methods by a
good margin.

这项研究通过动态主题跟踪任务将回复选择作为一种新的表述方法，并提出了一种支持高效编码的新型多任务学习框架，并且通过预训练模型，仅使用两次讲话来进行动态主题分离和回复选择。同时，还提出了 Topic-BERT 的关键预训练步骤来嵌入主题信息，并在 DSTC-8 Ubuntu IRC 数据集上获得了表现出色的结果。