Long-term Control for Dialogue Generation: Methods and Evaluation

TL;DR

This paper introduces a new framework for long-term controlled dialogue generation, emphasizing the importance of maintaining control over specific words over extended conversations, and proposes novel metrics and a retrieval-augmented method to improve performance.

Contribution

It defines the problem of constrained long-term dialogue control, introduces new evaluation metrics, and presents a retrieval-augmented approach that outperforms existing baselines.

Findings

New metrics better measure long-term control

Proposed method improves generation quality

Outperforms state-of-the-art baselines

Abstract

Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constrained long-term dialogue generation, which involves more fine-grained control and requires a given set of control words to appear in generated responses. This setting requires a model to not only consider the generation of these control words in the immediate context, but also produce utterances that will encourage the generation of the words at some time in the (possibly distant) future. We define the problem of constrained long-term control for dialogue generation, identify gaps in current methods for evaluation, and propose new metrics that better measure long-term control. We also propose a retrieval-augmented method that improves performance of long-term controlled generation via logit modification techniques. We show through experiments on three task-oriented dialogue datasets that our metrics better assess dialogue control relative to current alternatives and that our method outperforms state-of-the-art constrained generation baselines.