Commonsense Reasoning without Commonsense Knowledge
Liz Merkhofer, MITRE
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.
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.