Exploring Dense Retrieval for Dialogue Response Selection.

The article focuses on exploring dense retrieval for dialogue response selection, addressing challenges in ranking response candidates efficiently and improving response quality. Topics include the development of an efficient response selection model with dense retrieval and interaction layers, the use of learning strategies for effective training, and the release of high-quality benchmarks for accurate evaluation of dialogue response selection models.