  The impressive capabilities of recent generative models to create texts that
are challenging to distinguish from the human-written ones can be misused for
generating fake news, product reviews, and even abusive content. Despite the
prominent performance of existing methods for artificial text detection, they
still lack interpretability and robustness towards unseen models. To this end,
we propose three novel types of interpretable topological features for this
task based on Topological Data Analysis (TDA) which is currently understudied
in the field of NLP. We empirically show that the features derived from the
BERT model outperform count- and neural-based baselines up to 10\% on three
common datasets, and tend to be the most robust towards unseen GPT-style
generation models as opposed to existing methods. The probing analysis of the
features reveals their sensitivity to the surface and syntactic properties. The
results demonstrate that TDA is a promising line with respect to NLP tasks,
specifically the ones that incorporate surface and structural information.
