SemLa: A Visual Analysis System for Fine-grained Text Classification

Munkhtulga Battogtokh, Cosmin Davidescu, Michael Luck, Rita Borgo

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Fine-grained text classification requires models to distinguish
between many fine-grained classes that are hard to tell apart.
However, despite the increased risk of models relying on con-
founding features and predictions being especially difficult to
interpret in this context, existing work on the interpretability
of fine-grained text classification is severely limited. There-
fore, we introduce our visual analysis system, SemLa, which
incorporates novel visualization techniques that are tailored
to this challenge. Our evaluation based on case studies and
expert feedback shows that SemLa can be a powerful tool for
identifying model weaknesses, making decisions about data
annotation, and understanding the root cause of errors.
Original languageEnglish
Title of host publicationProceedings of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI’24)
Publication statusAccepted/In press - 2024


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