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Understanding how events are semantically related to each other is theessence of reading comprehension. Recent event-centric reading comprehensiondatasets focus mostly on event arguments or temporal relations. While thesetasks partially evaluate machines' ability of narrative understanding,human-like reading comprehension requires the capability to process event-basedinformation beyond arguments and temporal reasoning. For example, to understandcausality between events, we need to infer motivation or purpose; to establishevent hierarchy, we need to understand the composition of events. To facilitatethese tasks, we introduce ESTER, a comprehensive machine reading comprehension(MRC) dataset for Event Semantic Relation Reasoning. The dataset leveragesnatural language queries to reason about the five most common event semanticrelations, provides more than 6K questions and captures 10.1K event relationpairs. Experimental results show that the current SOTA systems achieve 22.1%,63.3%, and 83.5% for token-based exact-match, F1, and event-based HIT@1 scores,which are all significantly below human performances (36.0%, 79.6%, 100%respectively), highlighting our dataset as a challenging benchmark.

ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning