论文标题
追求目标：扎根足球评论的资源
Going for GOAL: A Resource for Grounded Football Commentaries
论文作者
论文摘要
最近的视频+语言数据集涵盖了相互作用高度结构化的域，例如教学视频或互动脚本（例如电视节目）。这两种属性都可能导致模型利用伪造线索，而不是学习地语言。在本文中，我们介绍了扎根的足球评论（Goal），这是一个新颖的足球数据集（或“足球”），以英语抄录了现场评论，以重点介绍视频。由于游戏的过程是不可预测的，因此评论也是如此，这使它们成为研究动态语言基础的独特资源。我们还为以下任务提供了最先进的基线：框架重新排序，时刻检索，实时评论检索和逐场播放现场评论生成。结果表明，SOTA模型在大多数任务中的表现都很好。我们讨论了这些结果的含义，并提出了可以使用哪个目标的新任务。我们的代码库可在以下网址找到：https：//gitlab.com/grounded-sport-convai/goal-baselines。
Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both of these properties can lead to spurious cues to be exploited by models rather than learning to ground language. In this paper, we present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or `soccer') highlights videos with transcribed live commentaries in English. As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding. We also provide state-of-the-art baselines for the following tasks: frame reordering, moment retrieval, live commentary retrieval and play-by-play live commentary generation. Results show that SOTA models perform reasonably well in most tasks. We discuss the implications of these results and suggest new tasks for which GOAL can be used. Our codebase is available at: https://gitlab.com/grounded-sport-convai/goal-baselines.
