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Found 7 papers, 2 papers with code
BERT-PersNER: A New Model for Persian Named Entity Recognition
Besides, as the very first effort to try active learning in the Persian NER, using only 30% of Arman and 20% of Peyma, we respectively achieved 92. 15%, and 92. 41% performance of the mentioned supervised learning experiments.
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers
In this paper, we explore the effect of quantization on the robustness of Transformer-based models.
Improving Question Answering Performance Using Knowledge Distillation and Active Learning
Contemporary question answering (QA) systems, including transformer-based architectures, suffer from increasing computational and model complexity which render them inefficient for real-world applications with limited resources.
LexiPers: An ontology based sentiment lexicon for Persian
The results show an acceptable performance in terms of accuracy and F-measure in the generated sentiment lexicon.
Towards Unsupervised Learning of Temporal Relations between Events
We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events.
