A multi-turn dialogue is composed of multiple utterances from two or more
different speaker roles. Thus utterance- and speaker-aware clues are supposed
to be well captured in models. However, in the existing retrieval-based
multi-turn dialogue modeling, the pre-trained language models (PrLMs) as
encoder represent the dialogues coarsely by taking the pairwise dialogue
history and candidate response as a whole, the hierarchical information on
either utterance interrelation or speaker roles coupled in such representations
is not well addressed. In this work, we propose a novel model to fill such a
gap by modeling the effective utterance-aware and speaker-aware representations
entailed in a dialogue history. In detail, we decouple the contextualized word
representations by masking mechanisms in Transformer-based PrLM, making each
word only focus on the words in current utterance, other utterances, two
speaker roles (i.e., utterances of sender and utterances of receiver),
respectively. Experimental results show that our method boosts the strong
ELECTRA baseline substantially in four public benchmark datasets, and achieves
various new state-of-the-art performance over previous methods. A series of
ablation studies are conducted to demonstrate the effectiveness of our method.

本文提出了一种新型方法，旨在通过建模对话历史中蕴含的有效话语意识和发言人意识表示，从而解决现有基于检索的多轮对话建模中针对言语和说话人角色的细粒度表示不足的问题，并在四个公共基准数据集上显著提高了 ELECTRA 基线的性能，达到了多种新的最新性能。