Commentary Generation from Data Records of Multiplayer Strategy Esports Game (2024)

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.

## introduction esports @xcite , a sports competition using video games, has become popular and gained a larger audience than ever. however, the individual gameplays accompany a few metadata such as player names, which prevents their audience from finding games with strategies of interest and understanding the intention of skillful actions. although textual game commentaries will help the audience retrieve games by a natural language query and better understand the player's actions (figure 1 ) @xcite , it is costly for human experts to provide individual games with play-by-play commentaries. as a result, only a small fraction of esports games with play logs accompany textual commentaries. to enhance the audience's experience in watching such esports games, the technology of datato-text generation can be used to generate game screenshot of "ward_placed" event (for explanation): input (one-minute structured data; an excerpt): { ... {" type ": " ward_placed ", " timestamp ": 905433 , " wardtype ": " yellowtrinket ", " creatorid ": 6 }, ... } output (play-by-play commentary; an excerpt): . . . even in a map state g2 can get exclusive vision on an area then suddenly the nautilus veigar will have a lot of zone control but so behind in map control . . . commentaries from structured data records. in the literature, data-to-text generation has been applied in summary generation from box-and linescores of basketball games @xcite and play-by-play commentary generation for board games @xcite . compared to the basketball data, esports data contains detailed game records and play-by-play commentaries. compared to board game data, esports data is usually not turn-based. in conclusion, there is a lack of research considering the characteristics of esports data in existing studies. in this study, we introduce the task of generating game commentaries from structured data records (figure 1 ) for one of the most popular esports games, league of legends (lol). broadly, the overall workflow for addressing this new task includes three main processes: we first build a largescale data-to-text generation dataset consisting of commentaries obtained from subtitles of youtube contest videos on esports games and corresponding structured data records obtained using lol official apis; we then use a transformer encoder-decoder model @xcite and its pre-trained variants to tackle the task; we also set evaluation metrics of esports data-to-commentary generation. we evaluate the performance of esports data-tocommentary generation on our proposed datasets using the metrics regarding the characteristics of esports data and discuss the main challenges of this task to be addressed in the future. the contributions of this paper are as follows: ## related work in this section, we review data-to-text generation tasks on sports games, board games, and video games to highlight the characteristics of our task. ## esports data-to-text datasets we have constructed and will release large-scale data-to-text datasets for one of the most popular esports games, league of legends (lol), which is also a demonstration sports event in the 2018 and 2022 asian games for its popularity (hallmann and giel, 2018; @xcite . the large-scale dataset is the core building block to assess the feasibility of data-to-text technology on the task. therefore, in this study, we build two datasets, lol19 and lol19-21, from all games in the highest-level tournaments of the 2019 and 2019 to 2021 season world championship of league of legends, respectively. in this section, we first introduce the basics of the target game, lol, and then explain methods of collecting data records and textual commentaries of lol. we also explain several data preprocessing methods to improve the collected datasets. ## esports data-to-text generation in this section, we first perform experiments on esports data-to-text generation using the lol19 dataset and several transformer @xcite -based models. then, we analyze the system outputs to reveal the challenges of this task. ## conclusions this study set up the task of generating game commentaries from structured data of the multiplayer strategy game, league of legends. we built and will release the first large-scale data-to-text generation dataset on strategic esports games. next, we also discussed evaluation metrics for our task to measure the quality and strategic depth of the system outputs. then, we explored the performance of transformer and its pre-trained variants for this task, revealing the challenges of the task such as associating past events with ongoing events. we will address the remaining issues in the future. the unique challenges include i) linking game history like past events related to current events, and ii) integrating visual inputs, including screenshots and video clips, to this task to perform crossmodal understanding and generation.