Detecting Online Risks and Supportive Interaction in Instant Messenger Conversations using Czech Transformers

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Authors

SOTOLÁŘ Ondřej PLHÁK Jaromír TKACZYK Michal LEBEDÍKOVÁ Michaela ŠMAHEL David

Year of publication 2021
Type Article in Proceedings
Conference Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)
MU Faculty or unit

Faculty of Informatics

Citation
Web
Keywords Online Risks; Supportive Interaction; Facebook Messenger; Text Classification
Description We present a comparison of state-of-the-art models for text clas- sification of Online Risks and Supportive Interaction in anonymized In- stant Messenger conversations held in Czech. We compare the transformer models Czert, RobeCzech, and FERNET-C5 with the Fasttext classifier as a baseline. For the comparison, we build a novel dataset with five sub- categories for the Online Risks and five for the Supportive Interaction. We solve the balanced classification problem achieving 75.44 - 89.66 F1 score depending on the category. Our results show that the transformer models perform consistently better than the baseline.
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