mlsanityData to 14 Oct 2026

BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bengali

arXiv 2310.10781 ↗First posted 16 Oct 2023cs.CLcited by 0

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Abstract (arXiv)

This paper presents the system that we have developed while solving this shared task on violence inciting text detection in Bangla. We explain both the traditional and the recent approaches that we have used to make our models learn. Our proposed system helps to classify if the given text contains any threat. We studied the impact of data augmentation when there is a limited dataset available. Our quantitative results show that finetuning a multilingual-e5-base model performed the best in our task compared to other transformer-based architectures. We obtained a macro F1 of 68.11% in the test set and our performance in this shared task is ranked at 23 in the leaderboard.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 16 Oct 2023.

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6 of 15 references.

References · 6

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  5. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  6. 2022BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contextscited by 14

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