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BERT is a popular language model whose main pre-training task is to fill inthe blank, i.e., predicting a word that was masked out of a sentence, based onthe remaining words. In some applications, however, having an additionalcontext can help the model make the right prediction, e.g., by taking thedomain or the time of writing into account. This motivates us to advance theBERT architecture by adding a global state for conditioning on a fixed-sizedcontext. We present our two novel approaches and apply them to an industryuse-case, where we complete fashion outfits with missing articles, conditionedon a specific customer. An experimental comparison to other methods from theliterature shows that our methods improve personalization significantly.

Contextual BERT: Conditioning the Language Model Using a Global State