  Event Temporal Relation Extraction (ETRE) is paramount but challenging.
Within a discourse, event pairs are situated at different distances or the
so-called proximity bands. The temporal ordering communicated about event pairs
where at more remote (i.e., ``long'') or less remote (i.e., ``short'')
proximity bands are encoded differently. SOTA models have tended to perform
well on events situated at either short or long proximity bands, but not both.
Nonetheless, real-world, natural texts contain all types of temporal
event-pairs. In this paper, we present MulCo: Distilling Multi-Scale Knowledge
via Contrastive Learning, a knowledge co-distillation approach that shares
knowledge across multiple event pair proximity bands 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.
