Modeling Romanized Hindi and Bengali: Dataset Creation and Multilingual LLM Integration
This addresses the problem of handling Romanized script in social media and digital communication for speakers of Hindi and Bengali, which are widely spoken languages, though it is incremental as it builds on existing transliteration methods.
The paper tackles the challenge of transliterating Romanized Hindi and Bengali into native scripts by creating a dataset of 1.8 million Hindi and 1 million Bengali transliteration pairs and pre-training a multilingual LLM, resulting in significant improvements in BLEU and CER metrics over existing models.
The development of robust transliteration techniques to enhance the effectiveness of transforming Romanized scripts into native scripts is crucial for Natural Language Processing tasks, including sentiment analysis, speech recognition, information retrieval, and intelligent personal assistants. Despite significant advancements, state-of-the-art multilingual models still face challenges in handling Romanized script, where the Roman alphabet is adopted to represent the phonetic structure of diverse languages. Within the South Asian context, where the use of Romanized script for Indo-Aryan languages is widespread across social media and digital communication platforms, such usage continues to pose significant challenges for cutting-edge multilingual models. While a limited number of transliteration datasets and models are available for Indo-Aryan languages, they generally lack sufficient diversity in pronunciation and spelling variations, adequate code-mixed data for large language model (LLM) training, and low-resource adaptation. To address this research gap, we introduce a novel transliteration dataset for two popular Indo-Aryan languages, Hindi and Bengali, which are ranked as the 3rd and 7th most spoken languages worldwide. Our dataset comprises nearly 1.8 million Hindi and 1 million Bengali transliteration pairs. In addition to that, we pre-train a custom multilingual seq2seq LLM based on Marian architecture using the developed dataset. Experimental results demonstrate significant improvements compared to existing relevant models in terms of BLEU and CER metrics.