Machine translation of Indian languages is challenging due to several factors, including linguistic diversity, limited parallel data, language divergence, and complex morphology. Recently, large pre-trained multilingual models have shown promise in improving translation quality. In this paper, we conduct a large-scale study on applying large pre-trained models for English-Indic machine translation through transfer learning across languages and domains. This study systematically evaluates the practical gains these models can provide and analyzes their capabilities for the translation of the Indian language by transfer learning. Specifically, we experiment with several models, including Meta’s mBART, mBART-manyto-many, NLLB-200, M2M-100, and Google’s MT5. These models are fine-tuned on small, high-quality English-Indic parallel data across languages and domains. Our findings show that adapting large pre-trained models to particular languages by fine-tuning improves translation quality across the Indic languages, even for languages unseen during pretraining. Domain adaptation through continued fine-tuning improves results. Our study provides insights into utilizing large pretrained models to address the distinct challenges of MT of Indian languages.

The prevalence of deceptive and incongruent news headlines has highlighted their substantial role in the propagation of fake news, exacerbating the spread of both misinformation and disinformation. Existing studies on incongruity detection primarily concentrate on estimating the similarity between the encoded representation of headlines and the encoded representation or summary representative vector of the news body. In the process of obtaining the encoded representation of the news body, researchers often consider either sequential encoding or hierarchical encoding of the news body or to acquire a summary representative vector of the news body, they explore techniques like summarization or dual summarization methods. Nevertheless, when it comes to detecting partially incongruent news, dual summarization-based methods tend to outperform hierarchical encoding-based methods. On the other hand, for datasets focused on detecting fake news, where the hierarchical structure within a news article plays a crucial role, hierarchical encoding-based methods tend to perform better than summarization-based methods. Recognizing this contradictory performance of hierarchical encoding-based and summarizationbased methods across datasets with different characteristics, we introduced a novel approach called Multiset Dual Summarization (MDS). MDS combines the strengths of both hierarchical encoding and dual summarization methods to leverage their respective advantages. We conducted experiments on datasets with diverse characteristics, and our findings demonstrate that our proposed model outperforms established state-of-the-art baseline models.