Browsing by Author "Ravikiran, M."

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    Findings of the First Shared Task on Offensive Span Identification from Code-Mixed Kannada-English Comments
    (Association for Computational Linguistics (ACL), 2024) Ravikiran, M.; Rajalakshmi, R.; Chakravarthi, B.; Anand Kumar, M.A.; Thavareesan, S.
    Effectively managing offensive content is crucial on social media platforms to encourage positive online interactions. However, addressing offensive contents in code-mixed Dravidian languages faces challenges, as current moderation methods focus on flagging entire comments rather than pinpointing specific offensive segments. This limitation stems from a lack of annotated data and accessible systems designed to identify offensive language sections. To address this, our shared task presents a dataset comprising Kannada-English code-mixed social comments, encompassing offensive comments. This paper outlines the dataset, the utilized algorithms, and the results obtained by systems participating in this shared task. © 2024 Association for Computational Linguistics.
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    Findings of the Second Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments
    (Incoma Ltd, 2023) Ravikiran, M.; Ganesh, A.; Anand Kumar, M.; Rajalakshmi, R.; Chakravarthi, B.R.
    Maintaining effective control over offensive content is essential on social media platforms to foster constructive online discussions. Yet, when it comes to code-mixed Dravidian languages, the current prevalence of offensive content moderation is restricted to categorizing entire comments, failing to identify specific portions that contribute to the offensiveness. Such limitation is primarily due to the lack of annotated data and open source systems for offensive spans. To alleviate this issue, in this shared task, we offer a collection of Tamil-English code-mixed social comments that include offensive comments. This paper provides an overview of the released dataset, the algorithms employed, and the outcomes achieved by the systems submitted for this task. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.
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    Findings of the Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments
    (Association for Computational Linguistics (ACL), 2022) Ravikiran, M.; Chakravarthi, B.R.; Anand Kumar, M.; Sangeetha, S.; Rajalakshmi, R.; Thavareesan, S.; Ponnusamy, R.; Mahadevan, S.
    Offensive content moderation is vital in social media platforms to support healthy online discussions. However, their prevalence in code-mixed Dravidian languages is limited to classifying whole comments without identifying part of it contributing to offensiveness. Such limitation is primarily due to the lack of annotated data for offensive spans. Accordingly, in this shared task, we provide Tamil-English code-mixed social comments with offensive spans. This paper outlines the dataset so released, methods, and results of the submitted systems. © 2022 Association for Computational Linguistics.
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    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.
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    Overlapping word removal is all you need: revisiting data imbalance in hope speech detection
    (Taylor and Francis Ltd., 2024) RamakrishnaIyer LekshmiAmmal, H.; Ravikiran, M.; Nisha, G.; Balamuralidhar, N.; Madhusoodanan, A.; Anand Kumar, A.K.; Chakravarthi, B.R.
    Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-switched Tamil and Malayalam. In this paper, we present deep learning techniques using context-aware string embeddings for word representations and Recurrent Neural Network (RNN) and pooled document embeddings for text representation. We have evaluated and compared the three models for each language with different approaches. Our proposed methodology works fine and achieved higher performance than baselines. The highest weighted average F-scores of 0.93, 0.58, and 0.84 are obtained on the task organisers{'} final evaluation test set. The proposed models are outperforming the baselines by 3{\%}, 2{\%} and 11{\%} in absolute terms for English, Tamil and Malayalam respectively. © 2023 Informa UK Limited, trading as Taylor & Francis Group.