Language is a window to the person’s mind and soul. Surprisingly, while few would disagree with this statement, most behavior prediction and analysis models do not consider language usage. We develop models that do exactly this, considering both economics setups (where game theory predictions consider only the numerical incentive of the participants) as well as psychological and psychiatric challenges (e.g. predicting suicide risk in the general population based on social media postings). Our goal is to integrate linguistic signals along with other behavioral and medical signals, and provide better prediction capabilities along with improved understanding of the underlying phenomena.
A fundamental problem of machine and deep learning models in NLP is that of spurious correlations. Such heavily parametrized models often capture data-driven patterns that are correlated with their task variables, but these patterns have little connection to the actual task they are trying to perform.
This, in turn, substantially harms their generalization capacity. We hence develop methods that follow the causal inference methodology for improved model generalization, interpretation, and stability.
Domain adaptation is the problem of adapting an algorithm trained on one domain (training distribution) so that it can effectively process data from other domains (e.g. adapting a sentiment classification algorithm trained on book reviews so that it can perform well on reviews of patient experience in clinics). We consider various very challenging setups of domain adaptation, focusing on setups where very limited resources and knowledge of the target domains are available when training the algorithm.
