  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.
