MOBA-E2C: Generating MOBA Game Commentaries via Capturing Highlight Events from the Meta-Data
Dawei Zhang, Sixing Wu, Yao Guo, Xiangqun Chen · 2022
MOBA (Multiplayer Online Battle Arena) games such as Dota2 are currently one of the most popular e-sports gaming genres.Following professional commentaries is a great way to understand and enjoy a MOBA game.However, massive game competitions lack commentaries because of the shortage of professional human commentators.As an alternative, employing machine commentators that can work at any time and place is a feasible solution.Considering the challenges in modeling MOBA games, we propose a data-driven MOBA commentary generation framework, MOBA-E2C, allowing a model to generate commentaries based on the game meta-data.Subsequently, to alleviate the burden of collecting supervised data, we propose a MOBA-FuseGPT generator to generate MOBA game commentaries by fusing the power of a rule-based generator and a generative GPT generator.Finally, in the experiments, we take a popular MOBA game Dota2 as our case and construct a Chinese Dota2 commentary generation dataset Dota2-Commentary.Experimental results demonstrate the superior performance of our approach.To the best of our knowledge, this work is the first Dota2 machine commentator and Dota2-Commentary is the first dataset.