@inproceedings{gupta-etal-2023-dialguide,
title = "{D}ial{G}uide: Aligning Dialogue Model Behavior with Developer Guidelines",
author = "Gupta, Prakhar and
Liu, Yang and
Jin, Di and
Hedayatnia, Behnam and
Gella, Spandana and
Liu, Sijia and
Lange, Patrick and
Hirschberg, Julia and
Hakkani-Tur, Dilek",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.935/",
doi = "10.18653/v1/2023.findings-emnlp.935",
pages = "14031--14047",
abstract = "Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DialGuide, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer`s expectations and intent. We evaluate DialGuide on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-response-guideline triplets across two domains - chit-chat and safety. We provide baseline models for the tasks and benchmark their performance. We also demonstrate that DialGuide is effective in the dialogue safety domain, producing safe and engaging responses that follow developer guidelines."
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<abstract>Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DialGuide, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer‘s expectations and intent. We evaluate DialGuide on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-response-guideline triplets across two domains - chit-chat and safety. We provide baseline models for the tasks and benchmark their performance. We also demonstrate that DialGuide is effective in the dialogue safety domain, producing safe and engaging responses that follow developer guidelines.</abstract>
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%0 Conference Proceedings
%T DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
%A Gupta, Prakhar
%A Liu, Yang
%A Jin, Di
%A Hedayatnia, Behnam
%A Gella, Spandana
%A Liu, Sijia
%A Lange, Patrick
%A Hirschberg, Julia
%A Hakkani-Tur, Dilek
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F gupta-etal-2023-dialguide
%X Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DialGuide, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer‘s expectations and intent. We evaluate DialGuide on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-response-guideline triplets across two domains - chit-chat and safety. We provide baseline models for the tasks and benchmark their performance. We also demonstrate that DialGuide is effective in the dialogue safety domain, producing safe and engaging responses that follow developer guidelines.
%R 10.18653/v1/2023.findings-emnlp.935
%U https://aclanthology.org/2023.findings-emnlp.935/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.935
%P 14031-14047
Markdown (Informal)
[DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines](https://aclanthology.org/2023.findings-emnlp.935/) (Gupta et al., Findings 2023)
ACL
- Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, and Dilek Hakkani-Tur. 2023. DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14031–14047, Singapore. Association for Computational Linguistics.