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.

翻译：对话响应选择旨在根据给定的用户和系统对话历史，从多个候选中选择合适的响应。现有工作主要关注为交叉编码器量身定制的后训练与微调方法。然而，目前尚无针对密集编码器在对话响应选择中的后训练技术。我们认为，当基于密集对话系统（如BERT）的现有语言模型作为密集编码器时，其分别独立编码对话上下文和响应，难以实现两者表示的对齐。为此，我们提出Dial-MAE（对话上下文掩码自编码器），一种为对话响应选择中密集编码器量身定制的简洁而有效的后训练技术。Dial-MAE采用非对称编码器-解码器架构，将对话语义压缩为密集向量，从而更好地实现对话上下文与响应特征的对齐。实验表明，Dial-MAE具有高效性，在两个常用基准测试中均达到了最先进的性能。