CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks
This addresses the need for better evaluation of commonsense reasoning in real-world NLP applications, though it is incremental as it builds on existing datasets.
The authors tackled the problem of evaluating commonsense reasoning in artificial scenarios by introducing CRoW, a manually-curated, multi-task benchmark for real-world NLP tasks, finding a significant performance gap between NLP systems and humans.
Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these datasets formulate commonsense reasoning challenges in artificial scenarios that are not reflective of the tasks which real-world NLP systems are designed to solve. In this work, we present CRoW, a manually-curated, multi-task benchmark that evaluates the ability of models to apply commonsense reasoning in the context of six real-world NLP tasks. CRoW is constructed using a multi-stage data collection pipeline that rewrites examples from existing datasets using commonsense-violating perturbations. We use CRoW to study how NLP systems perform across different dimensions of commonsense knowledge, such as physical, temporal, and social reasoning. We find a significant performance gap when NLP systems are evaluated on CRoW compared to humans, showcasing that commonsense reasoning is far from being solved in real-world task settings. We make our dataset and leaderboard available to the research community at https://github.com/mismayil/crow.