Browsing by Author "Tarannum, Prerona"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    NN at CheckThat! 2023: Subjectivity in News Articles Classification with Transformer Based Models
    (CEUR Workshop Proceedings, 2023-08-31) Dey, Krishno; Tarannum, Prerona; Hasan, Md. Arid; Noori, Sheak Rashed Haider
    The CheckThat! Lab is a challenging lab designed to address the issue of disinformation. We participated in CheckThat! Lab Task 2, which is focused on classification of subjectivity in news articles. This shared task included datasets in six different languages, as well as a multilingual dataset created by combining all six languages. We followed standard preprocessing steps for Arabic, Dutch, English, German, Italian, Turkish, and multilingual text data. We employed a transformer-based pretrained model, specifically XLM-RoBERTa large, for our official submission to the CLEF Task 2. Our results were impressive, as we achieved the 1st, 1st, 2nd, 5th, 2nd, 2nd, and 3rd positions on the leaderboard for the multilingual, Arabic, Dutch, English, German, Italian, and Turkish text data, respectively. Furthermore, we also applied BERT and BERT multilingual (BERT-m) models to assess the subjectivity of the text data. Our study revealed that XLM-RoBERTa large outperformed BERT and BERT-m in all performance measures for this particular dataset provided in the shared task.
  • No Thumbnail Available
    Item
    Z-Index at CheckThat! 2023:
    (Daffodil International University, 2023-08) Tarannum, Prerona; Hasan, Md. Arid; Alam, Firoj; Noori, Sheak Rashed Haider
    "In this study, we report our participation in CheckThat! lab’s Task 1. The aim is to determine whether a claim made in either unimodal or multimodal content is worth fact-checking. We implemented standard preprocessing and fine-tuned the XLM-RoBERTa-large model. Additionally, we applied zero-shot learning and utilized a feed-forward network with embeddings for unimodal content. For subtask 1A submission, we used combined BERT-based models (BERT and BERT multilingual), ResNet50, and Feed Forward network and we ranked as 3rd (Arabic) and 5th (English). We used feed forward network with embeddings for subtask 1B submission and ranked as 3rd in Arabic and 6th in both English and Spanish. In further experiments, our evaluation shows that XLM-RoBERTa-large model outperforms the other models"