Exploring Dense Retrieval for Dialogue Response Selection

Understand

Recent progress in deep learning has continuously improved the accuracy of dialogue response selection.

• In particular, sophisticated neural network architectures are leveraged to capture the rich interactions between dialogue context and response candidates.
• While remarkably effective, these models also bring in a steep increase in computational cost.
• Consequently, such models can only be used as a re-rank module in practice.