Systematic Inequalities in Language Technology across the World's Languages
Details
Damián Blasi (Harvard University), Antonios Anastasopoulos (George Mason University) and Graham Neubig (Carnegie Mellon University)
Summary:
Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world's 6,500 languages. We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies (machine translation, language understanding, question answering, text-to-speech synthesis) as well as more linguistic NLP tasks (dependency parsing, morphological inflection). In the process, we (1) quantify disparities in the current state of NLP research, (2) explore some of its associated societal and academic factors, and (3) produce tailored recommendations for evidence-based policy making aimed at promoting more global and equitable language technologies.
Bios:
Damián E. Blasi is a Post-Doc at Harvard University working at the intersection of linguistics, anthropology, cognitive sciences, and data sciences. His work aims to understand which aspects of collective and individual human behavior shape the structure of languages.
Antonios Anastasopoulos is an Assistant Professor in Computer Science at George Mason University. His research is focused on low-resource settings, endangered languages, and cross-lingual learning.
Graham Neubig is an Associate Professor at the Carnegie Mellon University Language Technology Institute in the School of Computer Science. His research focuses machine learning approaches that are both linguistically motivated, and tailored to applications such as machine translation and natural language understanding.
We will share a link to join the talk closer to the date.