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Efficient Data Augmentation via lexical matching for boosting performance on Statistical Machine Translation for Indic and a Low-resource language

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Abstract

With the fast advancement of AI technology in recent years, many excellent Data Augmentation (DA) approaches have been investigated to increase data efficiency in Natural Language Processing (NLP). The reliance on a large amount of data prohibits NLP models from performing tasks such as labelling enormous amounts of textual data, which require a substantial amount of time, money, and human resources; hence, a better model requires more data. Text DA technique rectifies the data by extending it, enhancing the model's accuracy and resilience. A novel lexical-based matching approach is the cornerstone of this work; it is used to improve the quality of the Machine Translation (MT) system. This study includes resource-rich Indic (i.e., Indo-Aryan and Dravidian language families) to examine the proposed techniques. Extensive experiments on a range of language pairs depict that the proposed method significantly improves scores in the enhanced dataset compared to the baseline system's BLEU, METEOR and ROUGE evaluation scores.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the [AI4Bharat/indicnlp_catalog] repository, [https://github.com/AI4Bharat/indicnlp_catalog]

Notes

  1. https://www.cfilt.iitb.ac.in/iitb_parallel/

  2. https://github.com/joshua-decoder/indian-parallel-corpora

  3. https://github.com/moses-smt/mosesdecoder

  4. https://github.com/anoopkunchukuttan/indic_nlp_library

  5. https://github.com/kpu/kenlm

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The research received no grant/funding in public, commercial, or not-for-profit sectors.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology, Hamirpur, India

    Shefali Saxena, Ayush Gupta & Philemon Daniel

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  1. Shefali Saxena
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  2. Ayush Gupta
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Correspondence to Shefali Saxena.

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Saxena, S., Gupta, A. & Daniel, P. Efficient Data Augmentation via lexical matching for boosting performance on Statistical Machine Translation for Indic and a Low-resource language. Multimed Tools Appl 83, 64255–64269 (2024). https://doi.org/10.1007/s11042-023-18086-8

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