  Machine reading is a fundamental task for testing the capability of natural
language understanding, which is closely related to human cognition in many
aspects. With the rising of deep learning techniques, algorithmic models rival
human performances on simple QA, and thus increasingly challenging machine
reading datasets have been proposed. Though various challenges such as evidence
integration and commonsense knowledge have been integrated, one of the
fundamental capabilities in human reading, namely logical reasoning, is not
fully investigated. We build a comprehensive dataset, named LogiQA, which is
sourced from expert-written questions for testing human Logical reasoning. It
consists of 8,678 QA instances, covering multiple types of deductive reasoning.
Results show that state-of-the-art neural models perform by far worse than
human ceiling. Our dataset can also serve as a benchmark for reinvestigating
logical AI under the deep learning NLP setting. The dataset is freely available
at https://github.com/lgw863/LogiQA-dataset
