32 citations · 51 across the 7 of their papers we have counts for
9 papers · 1 filter
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Julia Kiseleva, Alexey Skrynnik, Artem Zholus +14
Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté +5
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedur…
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté +3
Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of succe…
Building Dynamic Knowledge Graphs from Text-based Games
Mikuláš Zelinka, Xingdi Yuan, Marc-Alexandre Côté +2
We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate el…
Interactive Language Learning by Question Answering
Xingdi Yuan, Marc-Alexandre Cote, Jie Fu +4
Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking componen…
Interactive Machine Comprehension with Information Seeking Agents
Xingdi Yuan, Jie Fu, Marc-Alexandre Cote +3
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue t…