Commonsense Reasoning without Commonsense Knowledge

Abstract:

Reading comprehension tasks measure the ability to answer questions that require inference from a free text story. This talk explores the two machine learning approaches included in MITRE’s submission to a shared task, SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. The first system is a logistic regression over simple features like question length and lexicalized patterns. The second is a recurrent neural network with attention, which uses a pretrained semantic space and learns to align words of the story, passage, and answers. The resulting ensemble system answers reading comprehension questions with 82.27% accuracy and ranked second in the task. This strong performance, despite limited use of external knowledge sources or explicit reasoning, raises questions about “commonsense knowledge” in this task.

Bio:

Liz Merkhofer is a lead computational linguist at the MITRE Corporation, where she focuses on neural models/deep learning. Her work especially focuses on representation and transfer learning, for applications ranging from monitoring mental health-related messages in social media, judicial adjudication support, textual similarity, and content-based recommendation. She holds a BA in Philosophy and Spanish from the University of Arizona and a MA in Linguistics for Georgetown University.