Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item NITK-IT_NLP@TamilNLP-ACL2022: Transformer based model for Offensive Span Identification in Tamil(Association for Computational Linguistics (ACL), 2022) LekshmiAmmal, H.R.; Ravikiran, M.; Anand Kumar, M.Offensive Span identification in Tamil is a shared task that focuses on identifying harmful content, contributing to offensiveness. In this work, we have built a model that can efficiently identify the span of text contributing to offensive content. We have used various transformer-based models to develop the system, out of which the fine-tuned MuRIL model was able to achieve the best overall character F1-score of 0.4489. © 2022 Association for Computational Linguistics.Item NITK-IT NLP at CheckThat! 2022: Window based approach for Fake News Detection using transformers(CEUR-WS, 2022) LekshmiAmmal, H.R.; Anand Kumar, A.M.Misinformation is a severe threat to society which mainly spreads through online social media. The amount of misinformation generated and propagated is much more than authentic news. In this paper, we have proposed a model for the shared task on Fake News Classification by CLEF2022 CheckThat! Lab1, which had mono-lingual Multi-class Fake News Detection in English and cross-lingual task for English and German. We employed a transformer-based model with overlapping window strides, which helped us to achieve 7th and 2nd positions out of 25 and 8 participants on the final leaderboard of the two tasks respectively. We got an F1 score of 0.2980 and 0.2245 against the top score of 0.3391 and 0.2898 for the two tasks. © 2022 Copyright for this paper by its authors.Item Overview of Shared Task on Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes(Association for Computational Linguistics (ACL), 2024) Chakravarthi, B.; Rajiakodi, S.; Ponnusamy, R.; Pannerselvam, K.; Anand Kumar, M.A.; Rajalakshmi, R.; LekshmiAmmal, H.R.; Kizhakkeparambil, A.; Kumar, S.S.; Sivagnanam, B.; Rajkumar, C.This paper offers a detailed overview of the first shared task on "Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes," organized as part of the LT-EDI@EACL 2024 conference. The task was set to classify misogynistic content and troll memes within online platforms, focusing specifically on memes in Tamil and Malayalam languages. A total of 52 teams registered for the competition, with four submitting systems for the Tamil meme classification task and three for the Malayalam task. The outcomes of this shared task are significant, providing insights into the current state of misogynistic content in digital memes and highlighting the effectiveness of various computational approaches in identifying such detrimental content. The top-performing model got a macro F1 score of 0.73 in Tamil and 0.87 in Malayalam. © 2024 Association for Computational Linguistics.