Multi-turn response selection is a task designed for developing dialogue
agents. The performance on this task has a remarkable improvement with
pre-trained language models. However, these models simply concatenate the turns
in dialogue history as the input and largely ignore the dependencies between
the turns. In this paper, we propose a dialogue extraction algorithm to
transform a dialogue history into threads based on their dependency relations.
Each thread can be regarded as a self-contained sub-dialogue. We also propose
Thread-Encoder model to encode threads and candidates into compact
representations by pre-trained Transformers and finally get the matching score
through an attention layer. The experiments show that dependency relations are
helpful for dialogue context understanding, and our model outperforms the
state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results
on UbuntuV2.

本文提出了一种用于将对话历史转换为线程的对话提取算法，同时采用预训练 Transformer 进行编码并通过注意力层来得到匹配分数，实验表明依赖关系有助于对话上下文的理解，此模型在 DSTC7 和 DSTC8 等任务上的表现优于现有方法。