@inproceedings{wang-etal-2024-njust,
title = "{NJUST}-{KMG} at {TRAC}-2024 Tasks 1 and 2: Offline Harm Potential Identification",
author = "Wang, Jingyuan and
Depp, Jack and
Yang, Yang",
editor = "Kumar, Ritesh and
Ojha, Atul Kr. and
Malmasi, Shervin and
Chakravarthi, Bharathi Raja and
Lahiri, Bornini and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the Fourth Workshop on Threat, Aggression {\&} Cyberbullying @ LREC-COLING-2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.trac-1.4",
pages = "27--31",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification
%A Wang, Jingyuan
%A Depp, Jack
%A Yang, Yang
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Malmasi, Shervin
%Y Chakravarthi, Bharathi Raja
%Y Lahiri, Bornini
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the Fourth Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F wang-etal-2024-njust
%X 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.
%U https://aclanthology.org/2024.trac-1.4
%P 27-31
Markdown (Informal)
[NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification](https://aclanthology.org/2024.trac-1.4) (Wang et al., TRAC-WS 2024)
ACL