Multi-Scale Contrastive Knowledge Co-Distillation for Event Temporal Relation Extraction
Event Temporal Relation Extraction (ETRE) is a crucial yet challenging problem. Event pairs are situated within a discourse at different distances, which we refer to as proximity bands. The temporal ordering communicated about event pairs situated at more remote (i.e., ``long'') or less remote (i.e., ``short'') proximity bands is encoded differently. SOTA ETRE models have tended to perform well on events situated at either short or long proximity bands, but not both. Yet, real-world, natural texts contain all types of temporal event-pairs. In this paper, we present MulCo: Multi-Scale Contrastive Knowledge Co-Distillation, a fusion approach that shares knowledge across multiple event pair proximity bands in order to improve performance on all types of temporal datasets. Our experimental results show that MulCo successfully integrates linguistic cues pertaining to temporal reasoning across both short and long proximity bands and achieves new state-of-the-art results on several ETRE benchmark datasets.
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