Cuadro de ponentes

Dirk Hovy is a professor in the Computing Sciences Department, the scientific director of the Data and Marketing Insights research unit, and the inaugural dean for Digital Transformation and AI of Bocconi University. Previously, he was faculty at the University of Copenhagen, got a PhD from USC’s Information Sciences Institute, and a linguistics master’s from Marburg university in Germany. Dirk is interested in what computers can tell us about language and what language can tell us about society. That involves ethical questions of bias and algorithmic fairness in AI. Dirk has authored over 150 articles on these topics, two textbooks on NLP in Python, and forthcoming title at MIT Press. Dirk has co-founded and organized several workshops (on computational social science, and ethics in NLP), was a local organizer for the EMNLP 2017 conference, and general chair of EMNLP 2025. He was awarded an ERC Starting Grant project 2020 for research on demographic bias in NLP. In his spare time, he enjoys cooking, leather working, and picking up heavy things to put them back down.

Title: Unsolved: Why the Most Interesting Problems in NLP Are Still Ahead

Abstract: Large language models now power everyday writing, search, tutoring, and decision support. It might seem that with all that, natural language processing is essentially solved. However, that ignores the nature of language. While LLMs are remarkably fluent and better than humans at information processing, scale and fluency are not the same as language understanding. Language is not just a vehicle for information transfer: it is shaped by context, norms, relationships, ideology, and communicative goals. In this talk, I will argue that we have solved one set of problems, only to be able to explore a new, even more interesting set of challenges along those dimensions. Drawing on recent work on disagreement, moral judgment, safety evaluation, and socioeconomic differences in AI use, I will show that current models capture linguistic patterns without fully modeling the social aspects of language. I will argue that this is a hopeful moment for the field: we are finally in a position to address richer questions about social reasoning, human diversity, and language in interaction. The central claim is that NLP is far from finished. Its next frontier is not simply better next-token prediction, but deeper engagement with the social nature of language and closer collaboration with the social sciences, opening exciting new research directions.