  Dialogue response selection aims to select an appropriate response from
several candidates based on a given user and system utterance history. Recent
studies have been improving the accuracy of dialogue response selection through
post-training, mostly relying on naive masked language modeling methods.
However, the recently developed generative methods have shown promising text
representation capabilities in IR community, which could potentially lead to
better dialogue semantics modeling. Thus, in this paper, we propose Dial-MAE
(Dialogue Contextual Masking Auto-encoder), a straightforward yet effective
post-training technique tailored for dialogue response selection. Dial-MAE uses
an asymmetric encoder-decoder architecture that learns to better compress the
semantics of the dialogue into dialogue-dense vectors. The process of Dial-MAE
involves a deep encoder creating a dialogue embedding with the masked dialogue
context, followed by a shallow decoder that uses this embedding along with the
highly masked response to restore the original response. Our experiments have
demonstrated that Dial-MAE is highly effective, achieving state-of-the-art
performance on two commonly evaluated benchmarks.

Computation and Language;Artificial Intelligence