  Retrieve-based dialogue response selection aims to find a proper response
from a candidate set given a multi-turn context. Pre-trained language models
(PLMs) based methods have yielded significant improvements on this task. The
sequence representation plays a key role in the learning of matching degree
between the dialogue context and the response. However, we observe that
different context-response pairs sharing the same context always have a greater
similarity in the sequence representations calculated by PLMs, which makes it
hard to distinguish positive responses from negative ones. Motivated by this,
we propose a novel \textbf{F}ine-\textbf{G}rained \textbf{C}ontrastive (FGC)
learning method for the response selection task based on PLMs. This FGC
learning strategy helps PLMs to generate more distinguishable matching
representations of each dialogue at fine grains, and further make better
predictions on choosing positive responses. Empirical studies on two benchmark
datasets demonstrate that the proposed FGC learning method can generally and
significantly improve the model performance of existing PLM-based matching
models.
