  We present two supervised (pre-)training methods to incorporate gloss
definitions from lexical resources into neural language models (LMs). The
training improves our models' performance for Word Sense Disambiguation (WSD)
but also benefits general language understanding tasks while adding almost no
parameters. We evaluate our techniques with seven different neural LMs and find
that XLNet is more suitable for WSD than BERT. Our best-performing methods
exceeds state-of-the-art WSD techniques on the SemCor 3.0 dataset by 0.5% F1
and increase BERT's performance on the GLUE benchmark by 1.1% on average.
