The field of natural language processing is chasing the wrong goal

At a typical annual meeting of the Association for Computational Linguistics (ACL), the program is a parade of titles like "A Structured Variational Autoencoder for Contextual Morphological Inflection." At this year's conference in July, though, something felt different--and it wasn't just the virtual format. Attendees' conversations were unusually introspective about the core methods and objectives of natural-language processing (NLP), the branch of AI focused on creating systems that analyze or generate human language. Papers in this year's new "Theme" track asked questions like: Are current methods really enough to achieve the field's ultimate goals? What even are those goals? My colleagues and I at Elemental Cognition, an AI research firm based in Connecticut and New York, see the angst as justified.