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Generating new events given context with correlated ones plays a crucial rolein many event-centric reasoning tasks. Existing works either limit their scopeto specific scenarios or overlook event-level correlations. In this paper, wepropose to pre-train a general Correlation-aware context-to-Event Transformer(ClarET) for event-centric reasoning. To achieve this, we propose three novelevent-centric objectives, i.e., whole event recovering, contrastiveevent-correlation encoding and prompt-based event locating, which highlightevent-level correlations with effective training. The proposed ClarET isapplicable to a wide range of event-centric reasoning scenarios, consideringits versatility of (i) event-correlation types (e.g., causal, temporal,contrast), (ii) application formulations (i.e., generation and classification),and (iii) reasoning types (e.g., abductive, counterfactual and endingreasoning). Empirical fine-tuning results, as well as zero- and few-shotlearning, on 9 benchmarks (5 generation and 4 classification tasks covering 4reasoning types with diverse event correlations), verify its effectiveness andgeneralization ability.

ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification