My research combines techniques in natural language processing, machine learning and theories in social science to better understand human language and build intelligent systems to support human-human and human-computer communication. In this talk, I will explain my research from two specific studies. The first part studies what makes language persuasive by introducing a semi-supervised neural network to recognize persuasion strategies in loan requests on crowdfunding platforms, and further designed neural encoder-decoder systems to automatically transforming inappropriately subjective framing into a neutral point of view. The second focuses on modeling how people seek and offer support via language in online cancer support communities and building interventions to support patient communication. Through these examples, I show how we can accurately and efficiently build better language technologies for social good.
Diyi Yang is an assistant professor in the School of Interactive Computing at Georgia Tech. She is interested in natural language processing (such as language generation, semantics, discourse), and social computing. Diyi received her PhD from the Language Technologies Institute at Carnegie Mellon University, and her bachelor's degree from Shanghai Jiao Tong University, China. She has published more than 40 papers at leading NLP/HCI conferences, and received several best paper award nominations from EMNLP 2015, ICWSM 2016, and SIGCHI 2019. She has served as an area chair for ACL, NAACL and CSCW conferences.