ECO Decoding: Entropy-Based Control for Controllability and Fluency in Controllable Dialogue Generation

ACL ARR 2025 May Submission5681 Authors

Abstract: Controllable Dialogue Generation (CDG) enables chatbots to generate responses with desired attributes, and weighted decoding methods have achieved significant success in the CDG task. However, using a fixed constant value to manage the bias of attribute probabilities makes it challenging to find an ideal control strength that satisfies both controllability and fluency. To address this issue, we propose ECO decoding Entropy-based COntrol, which dynamically adjusts the control strength at each generation step according to the model’s entropy in both the language model and attribute classifier probability distributions. Experimental results on DailyDialog and MultiWOZ datasets show that our method achieves improved control accuracy while maintaining fluency and grammar, outperforming previous decoding methods across various models and settings. Furthermore, ECO decoding alleviates probability interpolation issues in multi-attribute generation, demonstrating its robust performance in both single and multi-attribute scenarios.

Paper Type: Long

Research Area: Generation

Research Area Keywords: text-to-text generation, inference methods, interactive and collaborative generation

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 5681