Experience Grounds Language
This addresses a foundational problem in natural language processing for improving AI communication, but it is incremental as it builds on existing traditions.
The paper argues that current language understanding research is limited by focusing solely on text, and proposes that integrating physical and social context is essential for meaningful communication.
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.