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pro vyhledávání: '"Ravikiran, Manikandan"'
This paper investigates the effectiveness of sentence-level transformers for zero-shot offensive span identification on a code-mixed Tamil dataset. More specifically, we evaluate rationale extraction methods of Local Interpretable Model Agnostic Expl
Externí odkaz:
http://arxiv.org/abs/2205.06119
Autor:
Ravikiran, Manikandan, Chakravarthi, Bharathi Raja, Madasamy, Anand Kumar, Sivanesan, Sangeetha, Rajalakshmi, Ratnavel, Thavareesan, Sajeetha, Ponnusamy, Rahul, Mahadevan, Shankar
Offensive content moderation is vital in social media platforms to support healthy online discussions. However, their prevalence in codemixed Dravidian languages is limited to classifying whole comments without identifying part of it contributing to
Externí odkaz:
http://arxiv.org/abs/2205.06118
Autor:
LekshmiAmmal, Hariharan RamakrishnaIyer, Ravikiran, Manikandan, Nisha, Gayathri, Balamuralidhar, Navyasree, Madhusoodanan, Adithya, Madasamy, Anand Kumar, Chakravarthi, Bharathi Raja
Hope Speech Detection, a task of recognizing positive expressions, has made significant strides recently. However, much of the current works focus on model development without considering the issue of inherent imbalance in the data. Our work revisits
Externí odkaz:
http://arxiv.org/abs/2204.05488
Temporal Ensembling is a semi-supervised approach that allows training deep neural network models with a small number of labeled images. In this paper, we present our preliminary study on the effect of intraclass variability on temporal ensembling, w
Externí odkaz:
http://arxiv.org/abs/2008.08956
Autor:
Ravikiran, Manikandan, Muljibhai, Amin Ekant, Miyoshi, Toshinori, Ozaki, Hiroaki, Koreeda, Yuta, Masayuki, Sakata
In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system with the BERT classifier trained with twe
Externí odkaz:
http://arxiv.org/abs/2005.00295
Autor:
Ravikiran, Manikandan
Diversity in content and open-ended questions are inherent in complex assignments across online graduate programs. The natural scale of these programs poses a variety of challenges across both peer and expert feedback including rogue reviews. While t
Externí odkaz:
http://arxiv.org/abs/2004.01549
Autor:
Ravikiran, Manikandan
Complex assignments typically consist of open-ended questions with large and diverse content in the context of both classroom and online graduate programs. With the sheer scale of these programs comes a variety of problems in peer and expert feedback
Externí odkaz:
http://arxiv.org/abs/2003.07019
Autor:
Ravikiran, Manikandan
Peer Assessment is a task of analysis and commenting on student's writing by peers, is core of all educational components both in campus and in MOOC's. However, with the sheer scale of MOOC's & its inherent personalised open ended learning, automatic
Externí odkaz:
http://arxiv.org/abs/2001.10617
Autor:
Ravikiran, Manikandan
Learning Management Systems (LMS) and Educational Data Mining (EDM) are two important parts of online educational environment with the former being a centralised web-based information systems where the learning content is managed and learning activit
Externí odkaz:
http://arxiv.org/abs/2001.09830
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