Citation :

Anitha Parthiban, 2025. "Self-Supervised Learning for Low-Resource Language Understanding" International Journal of Computer Science & Information Technology  Volume 1, Issue 3: 35-46.

Abstract :

During the last several years natural language processing (NLP) has been revolutionized by a combination of advances in deep learning and the development of very large annotated corpora that are available to train on, particularly in high-resource languages like English, Chinese and Spanish. With the advent of such large-scale resources, researchers have trained language models which are capable of doing sentiment analysis, machine translation, question-answering and a lot more with nearly human level performance. Yet these advances have led to a significant disparity in development between resource-rich and low-resource languages—languages with limited text digitization, few annotated datasets, and fewer computational resources generally. But the difference is more than an annoyance for users of different languages; it threatens to spread inequality and disenfranchise many of the thousands of human languages that are spoken every day on digital platforms worldwide.This is infeasible for most low-resource languages, where traditional supervised learning pipelines are often resource-intensive and impractical due to the reliance on vast quantities of labeled data. Large datasets are a time consuming, expensive endeavour to annotate — one that is sometimes impossible given the scarcity of linguistic experts and highly proficient native speakers. Hence, the many low-resource languages which economic weight in technology terms nor critical sociological need has kept on the periphery of NLP research and commercial AI deployment. Self-supervised learning (SSL) brings a paradigm shift to this scenario, allowing models to learn rich language representations straightforwardly from raw text without any annotation. SSL models manage to surface deep linguistic patterns based on predicting marked attention masks, reconstructed sentences or similar vs. dissimilar meaning pairs without need for explicit annotation.In this paper, we explore self-supervised learning methods for enhancing understanding of low-resource languages. In particular, we introduce two leading SSL methods for language modeling —Masked Language Modeling (MLM) and Contrastive Learning—and alleviate them with the limitations and specifications of three identified low-resource languages. We stress the need for meticulously curated preprocessing — subword tokenization customised to the morphological richness of each language, and balanced sampling strategies that cater to domain bias in the corpora collected! Additionally, we investigate the impact of various model scales, numbers and types of training regimes, and learning rate schedules on the efficacy of SSL in low-data scenarios.Overview of MethodologyOur approach starts by collecting raw text data from the web, including Common Crawl archives as well as regional news sites and community-contributed data. We normalize, clean and language-specific tokenization the data as pre-processing for SSL pertraining. In terms of the model architecture, we used a Transformer encoder framework similar to BERT but designed for lower computational budgets with fewer layers and smaller embedding dimensions in order to maintain rich context. The whole pretraining task includes two steps: masking random tokens and predicting them based on the context which is called Masked Language Model (MLM) and learning contrastive sentence embeddings where a pair of sentences are pulled together or pushed apart in embedding space by how semantically similar way they are as illustration.Subsequently, we fine-tune the pretrained models on three down-stream tasks that are relevant for practical natural language processing (NLP) settings in low-resource environments: sentiment classification, named entity recognition (NER), and low-resource-to-English machine translation. The notice does not say how the tasks and datasets were chosen, beyond choosing them to “emphasize a variety of linguistic capabilities — from basic polarity detection at one extreme, to more complex entity recognition and syntactic-semantic transfer at the other.” Performance of the SSL-pretrained models is then further compared with two baselines — (1) vanilla supervised learning from scratch, and (2) multilingual pretrained models such as mBERT [49] and XLM-R [14] fine-tuned on selected target language.Experiments show that SSL-pretrained models surpass baseline performance (in this work, highest-accuracy-per-task) on all three tasks. For sentiment analysis, we provide up to +4–7% absolute gain in accuracy rates compared to multilingual baselines which suggests that language-sensitive SSL pretraining may help discover the geographical and situational modulations often overshadowed by high-resource languages in the context of a multilingual model. This is a much larger relative improvement in the NER task, suggesting that SSL is able to provide better lexical representations for individual named entities or local context cues of each low resource language. Our models perform better in terms of BLEU scores on the machine translation side than fine-tuning based on mBERT, which indicates that SSL can serve as a more effective base to adapt for sequence-to-sequence tasks with low data.Discussion - The notes made on these results bring a very important depth of knowledge, Presumably, monolingual SSL pretraining (with relatively limited text data) can yield language representations that are as good as or better than multilingual transfer for at least some low-resource languages. The success of SSL is largely dependent on the quality of pre-processing, subword vocabulary designed, as well as picking up hyperparameters carefully to prevent overfitting. Third, though SSL is quite effective, it is not a panacea: incredibly low-data scenarios with fewer than a couple of million tokens remain difficult for current methods and domain mismatch between pretraining and downstream tasks can truncate improvements.It may stand as an important case study perpetuating that self-supervised learning is a possible and scalable route toward bridging the performance gap in NLP between high-resource and low-resource languages. By taking away the reliance on expensive labeled data and instead utilizing the vast amounts of unlabeled text, SSL gives researchers and communities the ability to build language technologies that are both financially and linguistically inclusive. Beyond academic work, the benefits are clear: such progress would improve cross-cultural communication, meaningfully digitize ‘uncommon’ languages, and democratize AI-powered applications. In future work, we will investigate hybrid approaches which combine semi-supervised learning with cross-lingual transfer, synthetic text generation for data augmentation and community-driven corpus creation to alleviate the challenges associated with low-resource NLP.

References :

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Keywords :

Self-Supervised Learning, Low-Resource Languages, Natural Language Processing, Masked Language Modeling, Contrastive Learning, Transformer Models, Language Understanding, Monolingual Pretraining, Cross-Lingual Transfer.