Commentary Generation from Data Records of Multiplayer Strategy Esports Game

Zihan Wang, Naoki Yoshinaga


Abstract
Esports, a sports competition on video games, has become one of the most important sporting events. Although esports play logs have been accumulated, only a small portion of them accompany text commentaries for the audience to retrieve and understand the plays. In this study, we therefore introduce the task of generating game commentaries from esports’ data records. We first build large-scale esports data-to-text datasets that pair structured data and commentaries from a popular esports game, League of Legends. We then evaluate Transformer-based models to generate game commentaries from structured data records, while examining the impact of the pre-trained language models. Evaluation results on our dataset revealed the challenges of this novel task. We will release our dataset to boost potential research in the data-to-text generation community.
Anthology ID:
2024.naacl-srw.28
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Yang (Trista) Cao, Isabel Papadimitriou, Anaelia Ovalle
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
263–271
Language:
URL:
https://aclanthology.org/2024.naacl-srw.28
DOI:
Bibkey:
Cite (ACL):
Zihan Wang and Naoki Yoshinaga. 2024. Commentary Generation from Data Records of Multiplayer Strategy Esports Game. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 263–271, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Commentary Generation from Data Records of Multiplayer Strategy Esports Game (Wang & Yoshinaga, NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-srw.28.pdf