Complexity of Symbolic Representation in Working Memory of Transformer
  Correlates with the Complexity of a Task
Authors: name: Alsu Sagirova, name: Mikhail Burtsev

Even though Transformers are extensively used for Natural Language Processing
tasks, especially for machine translation, they lack an explicit memory to
store key concepts of processed texts. This paper explores the properties of
the content of symbolic working memory added to the Transformer model decoder.
Such working memory enhances the quality of model predictions in machine
translation task and works as a neural-symbolic representation of information
that is important for the model to make correct translations. The study of
memory content revealed that translated text keywords are stored in the working
memory, pointing to the relevance of memory content to the processed text.
Also, the diversity of tokens and parts of speech stored in memory correlates
with the complexity of the corpora for machine translation task.