Why Do Neural Response Generation Models Prefer Universal Replies?

Understand

Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task.

• Despite its diverse applications, existing neural models are prone to producing short and generic replies, making it infeasible to tackle open-domain challenges.
• In this research, we analyze this critical issue in light of the model's optimization goal and the specific characteristics of the human-to-human dialog corpus.
• By decomposing the black box into parts, a detailed analysis of the probability limit was conducted to reveal the reason behind these universal replies.