  This paper presents a scoring system that has shown the top result on the
text subset of CALL v3 shared task. The presented system is based on text
embeddings, namely NNLM~\cite{nnlm} and BERT~\cite{Bert}. The distinguishing
feature of the given approach is that it does not rely on the reference grammar
file for scoring. The model is compared against approaches that use the grammar
file and proves the possibility to achieve similar and even higher results
without a predefined set of correct answers.
  The paper describes the model itself and the data preparation process that
played a crucial role in the model training.
