Get away with less: Need of source side data curation to build parallel corpus for low resource Machine Translation

TL;DR

This paper introduces LALITA, a novel framework for selecting source sentences based on lexical and linguistic features to efficiently create parallel corpora, significantly reducing data needs and improving low-resource machine translation quality.

Contribution

LALITA is a new sentence selection method that enhances low-resource MT by curating effective parallel data using linguistic insights, reducing data requirements by over 50%.

Findings

Training on complex sentences improves translation quality.

LALITA reduces data needs across multiple languages.

Method enhances MT performance with less data.

Abstract

Data curation is a critical yet under-researched step in the machine translation training paradigm. To train translation systems, data acquisition relies primarily on human translations and digital parallel sources or, to a limited degree, synthetic generation. But, for low-resource languages, human translation to generate sufficient data is prohibitively expensive. Therefore, it is crucial to develop a framework that screens source sentences to form efficient parallel text, ensuring optimal MT system performance in low-resource environments. We approach this by evaluating English-Hindi bi-text to determine effective sentence selection strategies for optimal MT system training. Our extensively tested framework, (Lexical And Linguistically Informed Text Analysis) LALITA, targets source sentence selection using lexical and linguistic features to curate parallel corpora. We find that by training mostly on complex sentences from both existing and synthetic datasets, our method significantly improves translation quality. We test this by simulating low-resource data availabilty with curated datasets of 50K to 800K English sentences and report improved performances on all data sizes. LALITA demonstrates remarkable efficiency, reducing data needs by more than half across multiple languages (Hindi, Odia, Nepali, Norwegian Nynorsk, and German). This approach not only reduces MT systems training cost by reducing training data requirement, but also showcases LALITA's utility in data augmentation.