The paper demonstrates the submission of the team nlpt_malayalm to the Fake News Detection in Dravidian Languages-DravidianLangTech@LT-EDI-2023. The rapid dissemination of fake news and misinformation in today’s digital age poses significant societal challenges. This research paper addresses the issue of fake news detection in the Malayalam language by proposing a novel approach based on the XLM-RoBERTa base model. The objective is to develop an effective classification model that accurately differentiates between genuine and fake news articles in Malayalam. The XLM-RoBERTa base model, known for its multilingual capabilities, is fine-tuned using the prepared dataset to adapt it specifically to the nuances of the Malayalam language. A thorough analysis is also performed to identify any biases or limitations in the model’s performance. The results demonstrate that the proposed model achieves a remarkable macro-averaged F-Score of 87% in the Malayalam fake news dataset, ranking 2nd on the respective task. This indicates its high accuracy and reliability in distinguishing between real and fake news in Malayalam.

The proliferation of fake news poses a significant challenge in the digital era. Detecting false information, especially in non-English languages, is crucial to combating misinformation effectively. In this research, we introduce a novel approach for Dravidian fake news detection by harnessing the capabilities of the MuRIL transformer model, further enhanced by gradient accumulation techniques. Our study focuses on the Dravidian languages, a diverse group of languages spoken in South India, which are often underserved in natural language processing research. We optimize memory usage, stabilize training, and improve the model’s overall performance by accumulating gradients over multiple batches. The proposed model exhibits promising results in terms of both accuracy and efficiency. Our findings underline the significance of adapting state-ofthe-art techniques, such as MuRIL-based models and gradient accumulation, to non-English languages to address the pressing issue of fake news.

Visual Question Answering (VQA) has recently become a popular research area. VQA problem lies in the boundary of Computer Vision and Natural Language Processing research domains. In VQA research, the dataset is a very important aspect because of its variety in image types i.e. natural and synthetic and also question answer source i.e. originated from human source or computer-generated question answer. Various details about each dataset is given in this paper, which can help future researchers to a great extent. In this paper, we discussed and compared the experimental performance of Stacked Attention Network Model (SANM) and bidirectional LSTM and MUTAN based fusion models. As per the experimental results, MUTAN accuracy and loss are 29% and 3.5 respectively. SANM model is giving 55% accuracy and a loss of 2.2 whereas VQA model is giving 59% accuracy and 1.9 loss.