How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?
<|reference_start|>How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?: Machine Translation (MT) between linguistically dissimilar languages is challenging, especially due to the scarcity of parallel corpora. Prior works suggest that pivoting through a high-resource language can help translation into a related low-resource language. However, existing works tend to discard the source sentence when pivoting. Taking the case of English to Indian language MT, this paper explores the 'multi-source translation' approach with pivoting, using both source and pivot sentences to improve translation. We conducted extensive experiments with various multi-source techniques for translating English to Konkani, Manipuri, Sanskrit, and Bodo, using Hindi, Marathi, and Bengali as pivot languages. We find that multi-source pivoting yields marginal improvements over the state-of-the-art, contrary to previous claims, but these improvements can be enhanced with synthetic target language data. We believe multi-source pivoting is a promising direction for Low-resource translation.<|reference_end|>
@article{gaikwad2024how,
    title={How effective is Multi-source pivoting for Translation of Low Resource
  Indian Languages?},
    author={Pranav Gaikwad, Meet Doshi, Raj Dabre, and Pushpak Bhattacharyya},
    journal={arXiv preprint arXiv:2406.13332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13332},
    primaryClass={cs.CL}
}