COSMO: Conditional SEQ2SEQ-based Mixture Model for Zero-Shot Commonsense Question Answering

Abstract: 

Commonsense reasoning refers to the ability of evaluating a social situation and acting accordingly. Identification of the implicit causes and effects of a social context is the driving capability which can enable machines to perform commonsense reasoning. The dynamic world of social interactions requires context-dependent on-demand systems to infer such underlying information. However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations. Hence, they fail to estimate the correct reasoning path. In this work, we present Conditional SEQ2SEQ-based Mixture model (COSMO), which provides us with the capabilities of dynamic and diverse content generation. We use COSMO to generate context-dependent clauses, which form a dynamic Knowledge Graph (KG) on-the-fly for commonsense reasoning. To show the adaptability of our model to context-dependant knowledge generation, we address the task of zero-shot commonsense question answering.

Biography: 

Farhad is currently a PhD candidate at Data Science group. He obtained his Bachelor and Master from Isfahan University of Technology and Queensland University of Technology, respectively. His research interests include causality, commonsense reasoning, and question answering systems.

Speaker: Farhad Moghimifar
Host: Dr Mahsa Baktashmotlagh