  The Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated
Text Detection shared task in the SemEval-2024 competition aims to tackle the
problem of misusing collaborative human-AI writing. Although there are a lot of
existing detectors of AI content, they are often designed to give a binary
answer and thus may not be suitable for more nuanced problem of finding the
boundaries between human-written and machine-generated texts, while hybrid
human-AI writing becomes more and more popular. In this paper, we address the
boundary detection problem. Particularly, we present a pipeline for augmenting
data for supervised fine-tuning of DeBERTaV3. We receive new best MAE score,
according to the leaderboard of the competition, with this pipeline.
