Differentiable Open-Ended Commonsense Reasoning

Abstract

Current commonsense reasoning research focuses on developing models that use commonsense knowledge to answer multiple-choice questions. However, systems designed to answer multiple-choice questions may not be useful in applications that do not provide a small list of candidate answers to choose from. As a step towards making commonsense reasoning research more realistic and useful, we propose to study open-ended commonsense reasoning (OpenCSR) — the task of answering a commonsense question without any predefined choices — using as a resource only a knowledge corpus of commonsense facts written in natural language. OpenCSR is challenging due to a large decision space, and because many questions require implicit multi-hop reasoning. As an approach to OpenCSR, we propose DRFACT, an efficient Differentiable model for multi-hop Reasoning over knowledge Facts. To evaluate OpenCSR methods, we adapt three popular multiple-choice datasets, and collect multiple new answers to each test question via crowd-sourcing. Experiments show that DRFACT outperforms strong baseline methods by a large margin.