Building Sequential Inference Models for End-to-End Response Selection

Understand

This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7).

• This task focuses on selecting the correct next utterance from a set of candidates given a partial conversation.
• We propose an end-to-end neural network based on enhanced sequential inference model (ESIM) for this task.
• Our proposed model differs from the original ESIM model in the following four aspects.