Ontologically Faithful Generation of Non-Player Character Dialogues
Nathaniel Weir, Ryan Thomas, Randolph d’Amore, Kellie Hill, Benjamin Van Durme, Harsh Jhamtani
Abstract
We introduce a language generation dataset grounded in a popular video game. KNUDGE (**KN**owledge Constrained **U**ser-NPC **D**ialogue **GE**neration) requires models to produce trees of dialogue between video game characters that accurately reflect quest and entity specifications stated in natural language. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment’s _The Outer Worlds_, leading to real-world complexities in generation: (1) utterances must remain faithful to the game lore, including character personas and backstories; (2) a dialogue must accurately reveal new quest details to the human player; and (3) dialogues are large trees as opposed to linear chains of utterances. We report results for a set of neural generation models using supervised and in-context learning techniques; we find competent performance but room for future work addressing the challenges of creating realistic, game-quality dialogues.
BibTeX
@inproceedings{weir-etal-2024-ontologically,
title = "Ontologically Faithful Generation of Non-Player Character Dialogues",
author = "Weir, Nathaniel and
Thomas, Ryan and
d{'}Amore, Randolph and
Hill, Kellie and
Van Durme, Benjamin and
Jhamtani, Harsh",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.520/",
doi = "10.18653/v1/2024.emnlp-main.520",
pages = "9212--9242"
}