Papers with cross-dataset evaluation
Enhancing Hate Speech Classifiers through a Gradient-assisted Counterfactual Text Generation Strategy (2025.findings-emnlp)
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| Challenge: | Strong attribute control can distort meaning, while prioritizing semantic preservation may weaken attribute alignment. |
| Approach: | They propose a method that restricts accepted samples to text meeting a minimum BERTScore threshold and applies gradient-assisted proposal generation to improve attribute alignment. |
| Outcome: | a new method for counterfactual text generation improves attribute alignment and semantic preservation . the proposed method achieved the best macro F1-score in two of three test sets . |
BanNERD: A Benchmark Dataset and Context-Driven Approach for Bangla Named Entity Recognition (2025.findings-naacl)
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Md. Motahar Mahtab, Faisal Ahamed Khan, Md. Ekramul Islam, Md. Shahad Mahmud Chowdhury, Labib Imam Chowdhury, Sadia Afrin, Hazrat Ali, Mohammad Mamun Or Rashid, Nabeel Mohammed, Mohammad Ruhul Amin
| Challenge: | In a cross-dataset evaluation, models trained on BanNERD consistently outperformed those trained on four existing Bangla NER datasets. |
| Approach: | They propose to use Bangla as a language to create the most extensive human-annotated and validated Bangla NLP dataset. |
| Outcome: | The proposed method outperforms existing methods on Bangla NER datasets and performs competitively on English datasets. |