  Text Generation Models (TGMs) succeed in creating text that matches human
language style reasonably well. Detectors that can distinguish between
TGM-generated text and human-written ones play an important role in preventing
abuse of TGM.
  In this paper, we describe our pipeline for the two DIALOG-22 RuATD tasks:
detecting generated text (binary task) and classification of which model was
used to generate text (multiclass task). We achieved 1st place on the binary
classification task with an accuracy score of 0.82995 on the private test set
and 4th place on the multiclass classification task with an accuracy score of
0.62856 on the private test set. We proposed an ensemble method of different
pre-trained models based on the attention mechanism.

Computation and Language