CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

TL;DR

CommonsenseQA introduces a challenging dataset for evaluating AI's ability to answer questions requiring commonsense knowledge, highlighting the gap between current models and human performance.

Contribution

The paper presents a new dataset with 12,247 questions designed to test commonsense reasoning, created via crowd-sourcing from ConceptNet relations.

Findings

BERT-large achieves 56% accuracy on the dataset

Human performance is 89% accuracy

The dataset reveals significant challenges for current models

Abstract

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from ConceptNet (Speer et al., 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge. We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines. Our best baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy, well below human performance, which is 89%.