Data-to-Text Generation for Esports Game Commentary of Multiplayer Strategy Game

Abstract

Esports, a sports competition on video games, has become one of the most important sporting events. Despite the large accumulation of esports play logs, only a small portion are accompanied by text commentaries that help the audience retrieve and understand the plays. In this study, we introduce the task of generating commentaries from esports game’s data records. We begin by building large-scale esports data-to-text datasets that pair structured data records with textual commentaries from a popular esports game, League of Legends. We then explore several generation models to produce game commentaries from structured data records while also examining the impact of pre-trained language models. To assess the generated commentaries, we designed evaluation metrics that focused on the unique characteristics of esports data, such as evaluating strategic depth. The experimental results of the data-to-text generation using our dataset revealed the remaining challenges of this novel task.