NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification
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Abstract (arXiv)
This report provide a detailed description of the method that we proposed in the TRAC-2024 Offline Harm Potential dentification which encloses two sub-tasks. The investigation utilized a rich dataset comprised of social media comments in several Indian languages, annotated with precision by expert judges to capture the nuanced implications for offline context harm. The objective assigned to the participants was to design algorithms capable of accurately assessing the likelihood of harm in given situations and identifying the most likely target(s) of offline harm. Our approach ranked second in two separate tracks, with F1 values of 0.73 and 0.96 respectively. Our method principally involved selecting pretrained models for finetuning, incorporating contrastive learning techniques, and culminating in an ensemble approach for the test set.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2021MuRIL: Multilingual Representations for Indian Languagescited by 177
- 2021Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transportcited by 28
- 2023Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformerscited by 3
- 2024LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representationscited by 3
- 2024HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Textcited by 1
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