  Unsupervised pretraining models have been shown to facilitate a wide range of
downstream NLP applications. These models, however, retain some of the
limitations of traditional static word embeddings. In particular, they encode
only the distributional knowledge available in raw text corpora, incorporated
through language modeling objectives. In this work, we complement such
distributional knowledge with external lexical knowledge, that is, we integrate
the discrete knowledge on word-level semantic similarity into pretraining. To
this end, we generalize the standard BERT model to a multi-task learning
setting where we couple BERT's masked language modeling and next sentence
prediction objectives with an auxiliary task of binary word relation
classification. Our experiments suggest that our "Lexically Informed" BERT
(LIBERT), specialized for the word-level semantic similarity, yields better
performance than the lexically blind "vanilla" BERT on several language
understanding tasks. Concretely, LIBERT outperforms BERT in 9 out of 10 tasks
of the GLUE benchmark and is on a par with BERT in the remaining one. Moreover,
we show consistent gains on 3 benchmarks for lexical simplification, a task
where knowledge about word-level semantic similarity is paramount.
