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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language\n Learning

2018/10/18 by Maxime Chevalier-Boisvert, Chevalier-Boisvert, Maxime, Dzmitry Bahdanau +11 · 2 voices · 69 citations
Chemistry · Computer Science · Psychology · #Artificial intelligence #Artificial neural network #Baseline (sea) #Chemistry #Computer science #Data science #Grounded theory #Heuristic #Human–computer interaction #Language acquisition #Language model #Machine learning #Mathematics education #Natural Language Processing Techniques #Natural language processing #Psychology #Qualitative research #Sample (material) #Social science #Sociology #Speech and dialogue systems #Suite #Topic Modeling #cs.AI #cs.CL

paper · pdf · doi:10.48550/arxiv.1810.08272

published in arXiv (Cornell University) (Cornell University) · Accepted at ICLR 2019

arxiv published 2018/10/18 · arxiv created 2019/12/19 · arxiv updated 2019/12/20

Abstract

Allowing humans to interactively train artificial agents to understand\nlanguage instructions is desirable for both practical and scientific reasons,\nbut given the poor data efficiency of the current learning methods, this goal\nmay require substantial research efforts. Here, we introduce the BabyAI\nresearch platform to support investigations towards including humans in the\nloop for grounded language learning. The BabyAI platform comprises an\nextensible suite of 19 levels of increasing difficulty. The levels gradually\nlead the agent towards acquiring a combinatorially rich synthetic language\nwhich is a proper subset of English. The platform also provides a heuristic\nexpert agent for the purpose of simulating a human teacher. We report baseline\nresults and estimate the amount of human involvement that would be required to\ntrain a neural network-based agent on some of the BabyAI levels. We put forward\nstrong evidence that current deep learning methods are not yet sufficiently\nsample efficient when it comes to learning a language with compositional\nproperties.\n

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