Salient Span Masking for Temporal Understanding

TL;DR

This paper explores how Salient Span Masking (SSM) and Temporal Span Masking (TSM) improve temporal understanding in language models, achieving state-of-the-art results by focusing on factual and temporal span representations.

Contribution

It introduces TSM as an extension of SSM for temporal tasks, demonstrating improved performance and analyzing the importance of sentence content in span masking strategies.

Findings

SSM improves temporal task performance by an average of 5.8 points.

Adding TSM yields an additional 0.29 point improvement.

Sentence content influences the effectiveness of span masking strategies.

Abstract

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.