Special Talk: Socially Responsible Language Technologies for Social Impact

Natural language processing (NLP) has had increasing success and produced extensive industrial applications. Despite being sufficient to enable these applications, current NLP systems often ignore the social part of language, e.g., who says it, in what context, for what goals, and with what social implications, all of which severely limits the functionality of these applications and the growth of the field. In this talk, I introduce socially aware NLP to show how we can study and build NLP systems from a social perspective using two specific studies. The first part looks at bias in NLP against underrepresented groups with a participatory design approach to develop dialect-inclusive language tools for low-resourced dialects. The second one examines how a negative perspective can be transformed into a more positive reframing for emotion regulation, as well as its implications for mental health and well-being. I conclude by discussing how to build this interdisciplinary subfield of socially aware language technologies for social impact.