Why Do Neural Response Generation Models Prefer Universal Replies?

TL;DR

This paper investigates why neural response generation models tend to produce generic replies, analyzes the underlying reasons, and proposes a max-margin ranking regularization to mitigate this issue, validated through experiments.

Contribution

It introduces a novel max-margin ranking regularization method to reduce universal replies in neural response models, based on detailed analysis of model behavior.

Findings

The regularization significantly reduces generic responses.

Models with the regularization produce more diverse and contextually relevant replies.

Experimental results outperform baseline models on multiple metrics.

Abstract

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. Based on these analyses, we propose a max-margin ranking regularization term to avoid the models leaning to these replies. Finally, empirical experiments on case studies and benchmarks with several metrics validate this approach.