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In this paper, we propose a neural architecture and a set of training methodsfor ordering events by predicting temporal relations. Our proposed modelsreceive a pair of events within a span of text as input and they identifytemporal relations (Before, After, Equal, Vague) between them. Given that a keychallenge with this task is the scarcity of annotated data, our models rely oneither pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer andmulti-task learning (by leveraging complementary datasets), and self-trainingtechniques. Experiments on the MATRES dataset of English documents establish anew state-of-the-art on this task.

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