  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, we propose to
study open-ended commonsense reasoning (OpenCSR) -- the task of answering a
commonsense question without any pre-defined choices -- using as a resource
only a 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 several popular commonsense reasoning
benchmarks, and collect multiple new answers for each test question via
crowd-sourcing. Experiments show that DrFact outperforms strong baseline
methods by a large margin.
