Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper presents a systematic approach for the retrieval of top-k relevant fact-checks for a given post in a monolingual and cross-lingual setup using transformer-based pre-trained models fine-tuned with a dual encoder architecture. By training and evaluating the shared task test dataset, our proposed best-performing framework achieved an average success@10 score of 0.79 and 0.62 for the retrieval of 10 fact-checks from the fact-check corpus against a post in monolingual and crosslingual track respectively.

With the advancement of natural language processing (NLP) and sophisticated Large Language Models (LLMs), distinguishing between human-written texts and machine-generated texts is quite difficult nowadays. This paper presents a systematic approach to classifying machine-generated text from human-written text with a combination of the transformer-based model and textual feature-based post-processing technique. We extracted five textual features: readability score, stop word score, spelling and grammatical error count, unique word score and human phrase count from both human-written and machine-generated texts separately and trained three machine learning models (SVM, Random Forest and XGBoost) with these scores. Along with exploring traditional machine-learning models, we explored the BiLSTM and transformer-based distilBERT models to enhance the classification performance. By training and evaluating with a large dataset containing both human-written and machine-generated text, our best-performing framework achieves an accuracy of 87.5%.