  Understanding the speaker's intended meaning often involves drawing
commonsense inferences to reason about what is not stated explicitly. In
multi-event sentences, it requires understanding the relationships between
events based on contextual knowledge. We propose COMET-M (Multi-Event), an
event-centric commonsense model capable of generating commonsense inferences
for a target event within a complex sentence. COMET-M builds upon COMET
(Bosselut et al., 2019), which excels at generating event-centric inferences
for simple sentences, but struggles with the complexity of multi-event
sentences prevalent in natural text. To overcome this limitation, we curate a
multi-event inference dataset of 35K human-written inferences. We trained
COMET-M on the human-written inferences and also created baselines using
automatically labeled examples. Experimental results demonstrate the
significant performance improvement of COMET-M over COMET in generating
multi-event inferences. Moreover, COMET-M successfully produces distinct
inferences for each target event, taking the complete context into
consideration. COMET-M holds promise for downstream tasks involving natural
text such as coreference resolution, dialogue, and story understanding.

Computation and Language;Artificial Intelligence