Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
Sohel and Shahriyar, Rifat , booktitle=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A language-adaptive combination of generalist, specialist, and ensemble transformer models achieves 0.796 macro F1 and 0.826 accuracy on multilingual polarization detection across 22 languages.
citing papers explorer
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Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text
Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
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MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization
A language-adaptive combination of generalist, specialist, and ensemble transformer models achieves 0.796 macro F1 and 0.826 accuracy on multilingual polarization detection across 22 languages.