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Collecting Interactive Multi-modal Datasets for Grounded Language Understanding

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arxiv 2211.06552 v3 pith:QQ2DV33W submitted 2022-11-12 cs.CL cs.AI

Shrestha Mohanty , Negar Arabzadeh , Milagro Teruel , Yuxuan Sun , Artem Zholus , Alexey Skrynnik , Mikhail Burtsev , Kavya Srinet
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Aleksandr Panov Arthur Szlam Marc-Alexandre Côté Julia Kiseleva
This is my paper · ORCID
classification cs.CLcs.AI
keywords languagefollowinggroundedinteractivenaturaltasksunderstandingacquire
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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