@inproceedings{wang-yoshinaga-2025-commentary,
title = "Commentary Generation from Multimodal Game Data for Esports Moments in Multiplayer Strategy Games",
author = "Wang, Zihan and
Yoshinaga, Naoki",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.112/",
pages = "1795--1807",
ISBN = "979-8-89176-303-6",
abstract = "Esports is a competitive sport in which highly skilled players face off in fast-paced video games. Matches consist of intense, moment-by-moment plays that require exceptional technique and strategy. These moments often involve complex interactions, including team fights, positioning, or strategic decisions, which are difficult to interpret without expert explanation. In this study, we set up the task of generating commentary for a specific game moment from multimodal game data consisting of a gameplay screenshot and structured JSON data. Specifically, we construct the first large-scale tri-modal dataset for League of Legends, one of the most popular multiplayer strategy esports titles, and then design evaluation criteria for the task. Using this dataset, we evaluate various large vision language models in generating commentary for a specific moment. We will release the scripts to reconstruct our dataset."
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%0 Conference Proceedings
%T Commentary Generation from Multimodal Game Data for Esports Moments in Multiplayer Strategy Games
%A Wang, Zihan
%A Yoshinaga, Naoki
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F wang-yoshinaga-2025-commentary
%X Esports is a competitive sport in which highly skilled players face off in fast-paced video games. Matches consist of intense, moment-by-moment plays that require exceptional technique and strategy. These moments often involve complex interactions, including team fights, positioning, or strategic decisions, which are difficult to interpret without expert explanation. In this study, we set up the task of generating commentary for a specific game moment from multimodal game data consisting of a gameplay screenshot and structured JSON data. Specifically, we construct the first large-scale tri-modal dataset for League of Legends, one of the most popular multiplayer strategy esports titles, and then design evaluation criteria for the task. Using this dataset, we evaluate various large vision language models in generating commentary for a specific moment. We will release the scripts to reconstruct our dataset.
%U https://aclanthology.org/2025.findings-ijcnlp.112/
%P 1795-1807
Markdown (Informal)
[Commentary Generation from Multimodal Game Data for Esports Moments in Multiplayer Strategy Games](https://aclanthology.org/2025.findings-ijcnlp.112/) (Wang & Yoshinaga, Findings 2025)
ACL