  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.
