Controlling Dialogue Generation with Semantic Exemplars
Prakhar Gupta, Jeffrey Bigham, Yulia Tsvetkov, Amy Pavel
Abstract
Dialogue systems pretrained with large language models generate locally coherent responses, but lack fine-grained control over responses necessary to achieve specific goals. A promising method to control response generation is exemplar-based generation, in which models edit exemplar responses that are retrieved from training data, or hand-written to strategically address discourse-level goals, to fit new dialogue contexts. We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide response generation. We show that controlling dialogue generation based on the semantic frames of exemplars improves the coherence of generated responses, while preserving semantic meaning and conversation goals present in exemplar responses.
BibTeX
@inproceedings{gupta-etal-2021-controlling,
title = "Controlling Dialogue Generation with Semantic Exemplars",
author = "Gupta, Prakhar and
Bigham, Jeffrey and
Tsvetkov, Yulia and
Pavel, Amy",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.240/",
doi = "10.18653/v1/2021.naacl-main.240",
pages = "3018--3029"
}