English

Experience Grounds Language

Computation and Language 2020-11-03 v3 Artificial Intelligence Machine Learning

Abstract

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

Keywords

Cite

@article{arxiv.2004.10151,
  title  = {Experience Grounds Language},
  author = {Yonatan Bisk and Ari Holtzman and Jesse Thomason and Jacob Andreas and Yoshua Bengio and Joyce Chai and Mirella Lapata and Angeliki Lazaridou and Jonathan May and Aleksandr Nisnevich and Nicolas Pinto and Joseph Turian},
  journal= {arXiv preprint arXiv:2004.10151},
  year   = {2020}
}

Comments

Empirical Methods in Natural Language Processing (EMNLP), 2020

R2 v1 2026-06-23T15:00:20.474Z