The societal needs of language technology users are rapidly changing alongside the technological landscape, calling for a deeper understanding of the impact of natural language processing models on user behaviors. As these technologies are increasingly deployed to users, it is ever more important to design culturally responsible and inclusive language technologies that can benefit a diverse population. Towards this end, I present three of our recent works: 1) Discourse-aware generation models for automatic social media moderation and mediation, 2) Sign language processing, and 3) Equitable and human-like dialogue generation models based on learning theory. Finally, I describe my research vision: Building inclusive and collaborative communicative systems and grounded artificial intelligence models by leveraging the cognitive science of language use alongside formal methods of machine learning.
Malihe Alikhani is an Assistant Professor of computer science in the School of Computing and Information at the University of Pittsburgh. She earned her Ph.D. in computer science with a graduate certificate in cognitive science from Rutgers University in 2020. Her research interests center on using representations of communicative structure, machine learning, and cognitive science to design practical and inclusive NLP systems for social good. Her work has received multiple best paper awards at ACL, UAI, INLG, and UMAP.