CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering
Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Mahsa Baktashmotlagh, Gholamreza Haffari · 2020
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 paper, 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.The empirical results indicate an improvement of up to +5.2% over the state-of-the-art models.