论文标题
收集交互式多模式数据集，以了解基础语言理解
Collecting Interactive Multi-modal Datasets for Grounded Language Understanding
论文作者
论文摘要
人类智能可以非常迅速地适应新的任务和环境。从很小的时候开始，人类就可以获得新技能，并学习如何通过模仿他人的行为或遵循提供的自然语言指示来解决新任务。为了促进可以在机器中实现类似功能的研究，我们采取了以下贡献（1）使用自然语言任务正式化了协作体现的代理； （2）开发了一种用于广泛可扩展数据收集的工具； （3）收集了第一个数据集，以进行交互式基础语言理解。
Human intelligence can remarkably adapt quickly to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research which can enable similar capabilities in machines, we made the following contributions (1) formalized the collaborative embodied agent using natural language task; (2) developed a tool for extensive and scalable data collection; and (3) collected the first dataset for interactive grounded language understanding.
