Self-supervision techniques have allowed neural language models to advance the frontier in Natural Language Understanding. However, existing self-supervision techniques operate at the word-form level, which serves as a surrogate for the underlying semantic content. This paper proposes a method to employ self-supervision directly at the word-sense level. Our model, named SenseBERT, is pre-trained to predict not only the masked words but also their WordNet supersenses. Accordingly, we attain a lexical-semantic level language model, without the use of human annotation. SenseBERT achieves significantly improved lexical understanding, as we demonstrate by experimenting on SemEval, and by attaining a state of the art result on the Word in Context (WiC) task. Our approach is extendable to other linguistic signals, which can be similarly integrated into the pre-training process, leading to increasingly semantically informed language models.
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