@inproceedings{2c6ad394fc414f059a501402d970ce52,
title = "Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue",
abstract = "Knowledge-grounded dialogue is a task of generating an informative response based on both the dialogue history and external knowledge source. In general, there are two forms of knowledge: manually annotated knowledge graphs and knowledge text from website. From various evaluation viewpoints, each type of knowledge has advantages and downsides. To further distinguish the principles and determinants from the intricate factors, we conduct a thorough experiment and study on the task to answer three essential questions. The questions involve the choice of appropriate knowledge form, the degree of mutual effects between knowledge and the model selection, and the few-shot performance of knowledge. Supported by statistical shreds of evidence, we offer conclusive solutions and sensible suggestions for directions and standards of future research.",
author = "Yizhe Yang and Heyan Huang and Yuhang Liu and Yang Gao",
note = "Publisher Copyright: {\textcopyright}2023 Association for Computational Linguistics.; 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",
year = "2023",
language = "English",
series = "EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "15846--15858",
editor = "Houda Bouamor and Juan Pino and Kalika Bali",
booktitle = "EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings",
address = "United States",
}