  Stories and narratives are composed based on a variety of events.
Understanding how these events are semantically related to each other is the
essence of reading comprehension. Recent event-centric reading comprehension
datasets focus on either event arguments or event temporal commonsense.
Although these tasks evaluate machines' ability of narrative understanding,
human like reading comprehension requires the capability to process event-based
semantics beyond arguments and temporal commonsense. For example, to understand
causality between events, we need to infer motivations or purposes; to
understand event hierarchy, we need to parse the composition of events. To
facilitate these tasks, we introduce ESTER, a comprehensive machine reading
comprehension (MRC) dataset for Event Semantic Relation Reasoning. We study
five most commonly used event semantic relations and formulate them as question
answering tasks. Experimental results show that the current SOTA systems
achieve 60.5%, 57.8%, and 76.3% for event-based F1, token based F1 and HIT@1
scores respectively, which are significantly below human performances.
