  Dialogue response selection aims to select an appropriate response from
several candidates based on a given user and system utterance history. Most
existing works primarily focus on post-training and fine-tuning tailored for
cross-encoders. However, there are no post-training methods tailored for dense
encoders in dialogue response selection. We argue that when the current
language model, based on dense dialogue systems (such as BERT), is employed as
a dense encoder, it separately encodes dialogue context and response, leading
to a struggle to achieve the alignment of both representations. Thus, we
propose Dial-MAE (Dialogue Contextual Masking Auto-Encoder), a straightforward
yet effective post-training technique tailored for dense encoders in dialogue
response selection. Dial-MAE uses an asymmetric encoder-decoder architecture to
compress the dialogue semantics into dense vectors, which achieves better
alignment between the features of the dialogue context and response. Our
experiments have demonstrated that Dial-MAE is highly effective, achieving
state-of-the-art performance on two commonly evaluated benchmarks.
