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Building intelligent agents that can communicate with and learn from humans in natural language is of great value.
We build a virtual agent for learning language in a 2D maze-like world.
Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms.
Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks - English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en).
SOTA for Language Acquisition on SLAM 2018
We introduce a newly collected data set of human semantic relevance judgements and an associated task, semantic speech retrieval, where the goal is to search for spoken utterances that are semantically relevant to a given text query.
Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground.