A comprehensive study on claim detection utilizing multi-task learning

Abstract

This work delves into the dynamic domain of Natural Language Processing (NLP), which has experienced remarkable strides owing to the advent of machine learning and deep learning methodologies. Within this expansive field, the identification and assessment of claims within textual data have emerged as pivotal tasks, necessitating automated solutions to gauge their validity and relevance. Our exploration extends beyond the confines of English to encompass a spectrum of languages, with a keen focus on Indian linguistic diversity, thereby broadening the applicability of NLP techniques across diverse cultural and linguistic contexts. Central to our investigation is the paradigm of Multi-task Learning (MTL), which offers the promise of concurrent task execution to enhance overall model performance. While MTL has traditionally been applied to similar tasks, our research pioneers its application to dissimilar tasks, thereby pushing the boundaries of its utility and efficacy. To this end, we employ innovative fusion methodologies and a long-head approach in task-specific layer strategies, helping our MTL models with enhanced adaptability and accuracy. Furthermore, our study delves into the intricate domain of Span Identification, particularly honing in on Sentiment and Claim Span Identification tasks. Leveraging transfer learning techniques, we develop robust models capable of effectively extracting specific spans of text containing pertinent information across multiple languages. By imbuing our models with multilingual support, we strive to ensure their versatility and applicability in diverse linguistic contexts, particularly within the rich variety of the Indian linguistic landscape. In synthesizing these methodologies and approaches, our research contributes to the advancement of NLP applications, fostering greater understanding and automation of textual data analysis, especially within the intricate and diverse linguistic fabric of India.