@inproceedings{tamali-etal-2023-comparing,
    title = "Comparing {DAE}-based and {MASS}-based {UNMT}: Robustness to Word-Order Divergence in {E}nglish{--}{\ensuremath{>}}{I}ndic Language Pairs",
    author = "Banerjee, Tamali  and
      Murthy, Rudra  and
      Bhattacharyya, Pushpak",
    editor = "D. Pawar, Jyoti  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2023",
    address = "Goa University, Goa, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.icon-1.44/",
    pages = "491--496",
    abstract = "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-of-the-art techniques, such as MuRIL-based models and gradient accumulation, to non-English language."
}