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.

本文提出了约束性的、long-term控制对话生成的问题，并提出了一种检测其性能的新指标，并采用改进的基于检索的方法，该方法通过修改logits来提高长期控制的生成性能。通过在三个面向任务的对话数据集上的实验证明了我们的指标相对于当前的替代方案更好地评估了对话控制，并且我们的方法优于最先进的约束性生成基线。

对话生成的长期控制：方法与评估