论文标题
从多人游戏策略电子竞技游戏的数据记录中生成评论
Commentary Generation from Data Records of Multiplayer Strategy Esports Game
论文作者
论文摘要
电子竞技是一项视频游戏的体育比赛，已成为最重要的体育赛事之一。尽管已经积累了电子竞技播放日志，但只有一小部分伴随文字评论，以供观众检索和理解这些游戏。因此，在这项研究中，我们介绍了从电子竞技记录中产生游戏评论的任务。我们首先构建了大规模的电子竞技数据对文本数据集，该数据集将构造数据和评论配对流行的电子竞技游戏《英雄联盟》。然后，我们评估基于变压器的模型，以从结构化数据记录中生成游戏评论，同时研究预训练的语言模型的影响。我们数据集的评估结果揭示了这一新任务的挑战。我们将发布我们的数据集，以促进数据到文本生成社区的潜在研究。
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.
