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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Multilingual and Multimodal Abuse Detection ... Sharon, Rini; Shah, Heet; Mukherjee, Debdoot. - : arXiv, 2022 BASE Show details 2 3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos ... Gupta, Vikram; Mittal, Trisha; Mathur, Puneet. - : arXiv, 2022 BASE Show details 3 ADIMA: Abuse Detection In Multilingual Audio ... Gupta, Vikram; Sharon, Rini; Sawhney, Ramit. - : arXiv, 2022 BASE Show details 4 Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training ... Gupta, Vikram. - : arXiv, 2021 BASE Show details 5 Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gupta, Vikram. - : Underline Science Inc., 2021 Abstract: Virtual Adversarial Training (VAT) has been effective in learning robust models under supervised and semi-supervised settings for both computer vision and NLP tasks. However, the efficacy of VAT for multilingual and multilabel text classification has not been explored before. In this work, we explore VAT for multilabel emotion recognition with a focus on leveraging unlabelled data from different languages to improve the model performance. We perform extensive semi-supervised experiments on SemEval2018 multilabel and multilingual emotion recognition dataset and show performance gains of 6.2% (Arabic), 3.8% (Spanish) and 1.8% (English) over supervised learning with same amount of labelled data (10% of training data). We also improve the existing state-of-the-art by 7%, 4.5% and 1% (Jaccard Index) for Spanish, Arabic and English respectively and perform probing experiments for understanding the impact of different layers of the contextual models. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis URL: https://underline.io/lecture/39636-multilingual-and-multilabel-emotion-recognition-using-virtual-adversarial-training https://dx.doi.org/10.48448/40e0-wz37 BASE Hide details 6 Deep Clustering of Text Representations for Supervision-free Probing of Syntax ... Gupta, Vikram; Shi, Haoyue; Gimpel, Kevin. - : arXiv, 2020 BASE Show details

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