  Salient Span Masking (SSM) has shown itself to be an effective strategy to
improve closed-book question answering performance. SSM extends general masked
language model pretraining by creating additional unsupervised training
sentences that mask a single entity or date span, thus oversampling factual
information. Despite the success of this paradigm, the span types and sampling
strategies are relatively arbitrary and not widely studied for other tasks.
Thus, we investigate SSM from the perspective of temporal tasks, where learning
a good representation of various temporal expressions is important. To that
end, we introduce Temporal Span Masking (TSM) intermediate training. First, we
find that SSM alone improves the downstream performance on three temporal tasks
by an avg. +5.8 points. Further, we are able to achieve additional improvements
(avg. +0.29 points) by adding the TSM task. These comprise the new best
reported results on the targeted tasks. Our analysis suggests that the
effectiveness of SSM stems from the sentences chosen in the training data
rather than the mask choice: sentences with entities frequently also contain
temporal expressions. Nonetheless, the additional targeted spans of TSM can
still improve performance, especially in a zero-shot context.
