Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events

In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them... (read more)

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