Did you know, of the world's approximately 7,000+ languages, fewer than 100 can be considered high-resource languages? In today's world, language serves as both a bridge and a barrier. While major languages like English, Spanish, and Mandarin enjoy extensive digital resources, thousands of languages remain underserved by modern technology. These are known as low-resource languages, and their integration into the digital world presents both challenges and opportunities.
In this comprehensive guide, you will have a complete overview of low-resource languages (LRLs), exploring what they are, why they matter, the difficulties involved in working with them, potential solutions, and the role of translation services companies in reducing the gap between low and high resource languages.
Your first impression about these must be that languages spoken by a small population are called low-resource languages. But that is not it. In fact, Hindi, spoken by almost 600 million people worldwide, is considered a low-resource language.
Low-resource languages are those for which limited linguistic data and resources are available for natural language processing. Training machine learning and developing effective language models requires enough linguistic resources, such as speech data and annotated text, which a low-resource language typically lacks.
AI is an integral part of the translation and localization industry. It helps enhance the speed and quality of translation while boosting the efficiency of translators. We all know this, but what you might not know is the back-end story.
Machines are trained to learn languages through natural language processing (NLP). In this process, a large amount of linguistic data is used to teach machines the structures, patterns, and semantics of languages.
So, languages with less digital presence, computational resources, and annotated datasets, and that are under-represented in academic research, fall in the category of low-resource languages. Here is the list of LRLs:
Now, you might be thinking if languages with huge populations fall in the LRL category, then which are the high-resource languages, right?
Basically, a language is classified as low or high-resource based on how much data is available for training AI language systems. As compared to LRLs, languages with extensive amounts of text and speech data, advanced NLP tools, significant research and development in language-related fields, and spoken by a large number of people globally fall in the HRL category, including:
In contrast, low-resource languages often suffer from minimal online presence, few digitized documents, and a lack of standardized writing systems. This digital divide affects billions of speakers worldwide, limiting their access to information, education, and economic opportunities.
A language is the essence of any nation and is intricately linked with its culture. It carries unique histories, artistic expressions, and worldviews, making every language important regardless of high or low resources.
Nearly 1500 out of the 7000 languages spoken worldwide are predicted to go extinct by the end of this century, according to recent research. They might have limited text and speech data and are less developed for natural language processing tools, so it isn’t right to let them fade away so easily. The decline of LRLs will cause the loss of invaluable cultural heritage. Low-resource languages are also important because they help in preserving linguistic diversity, which is a vital aspect of human civilization.
Moreover, they offer significant expansion opportunities for global businesses. For example, Swahili counts as an LRL, but being spoken by more than 200 million people, it offers a huge target audience and market. Preserving these languages is crucial. Therefore, we should focus on finding a way to preserve them while acknowledging the challenges involved and approaches to overcome them.
It is crucial to understand its root cause to address a problem effectively. Let’s take a look at the roadblocks that make it difficult to work with LRLs.
Training AI models in diverse domains such as literature, social media, and news requires a sufficient amount of data in the form of written text, audio, and videos. In low-resource languages, there is very limited availability of high-quality content crucial for speech recognition and synthesis.
The impact of training models with small datasets, such as limited dictionaries, grammars, and annotated corpora, will not be gratifying, as results may lead to poor generalization, perpetuating inequalities, and exhibiting lower accuracy, robustness, and fluency.
Other than the data scarcity, lack of specialized tools, pre-trained models, and computational resources are also huge challenges in language development. Moreover, there is a shortage of skilled linguists with expertise in these languages, which further aggravates these issues.
Variations in writing systems and dialects can hinder the development of consistent linguistic resources.
Low resources restrict ensuring cultural sensitivity in the development and deployment of NLP technologies to respect the unique linguistic and cultural nuances of these languages.
There is often minimal commercial interest and limited government support for developing language technologies for these languages.
The following are the ways used to overcome the challenges associated with low-resource languages in NLP:
This is the process of artificially increasing the training data size for language processing by modifying the available data. There are different methods used in this process called data augmentation techniques, including:
In this method, a low-resource language is first translated into a high-resource language and then again translated into an LRL. For example, Hindi-to-English-to-Hindi. Doing this often provides slightly different phrasing, which can be used as new training examples.
Another step for enhancing the availability of training data is replacing words with their synonyms.
This process involves adding a small amount of noise to the data in the form of typos as well as variations in pronunciation.
Labeling words with their part of speech and marking sentence structure is called an annotation. Although it is an expensive and time-consuming process, it is crucial for language processing. For LRLs where expert annotators may be scarce, unsupervised and semi-supervised learning techniques are used.
As the name suggests, in this process the algorithms learn patterns from unlabelled data. Language models are trained using raw text to capture semantic relationships between words even without explicit labels.
It involves combining a little amount of labeled data with a large amount of unlabelled data. The goal is for language models to learn from accessible labeled data and then utilize that knowledge to make sense of unlabelled data.
Recent advances in machine translation are helping address the low-resource challenge:
Modern AI systems can transfer knowledge from high-resource languages to related low-resource ones, improving translation quality with limited data.
New models can sometimes translate between language pairs they've never directly seen during training, opening possibilities for low-resource languages.
Platforms allowing native speakers to contribute translations and corrections help build valuable datasets for low-resource languages.
For instance, Google's integration of 31 African languages into Google Translate, including languages like Tamazight, Afar, Wolof, Dyula, and Baoulé, represents a significant step toward inclusivity. This initiative, which involved collaboration with linguists, NGOs, and communities, aims to encourage the use of native languages, particularly among the diaspora and younger generations.
The future of low-resource languages depends on continued innovation in machine translation and support from the global community. By combining traditional linguistic expertise with modern technology, we can work toward a more inclusive digital world where language barriers no longer limit access to information and opportunities.
Bridging the digital language divide between LRLs and HRLs requires coordinated effort from multiple stakeholders:
Investing in low-resource language support and developing inclusive technologies.
Researching efficient training methods for limited data and documenting linguistic resources.
Contributing language expertise and cultural context, and participating in data collection efforts.
Funding language preservation and digitization efforts, and implementing supportive language policies.
Translation agencies play a crucial role in bridging the gap between high-resource and low-resource languages. Their work encompasses several critical areas:
Professional translators generate high-quality parallel texts across multiple domains, providing essential data for training machine translation models.
Comprehensive documentation of grammatical rules, formatting conventions, and cultural nuances ensures consistency and quality in translations.
Agencies invest in building local expertise through structured training programs, fostering a pool of qualified translators.
Managing terminology is crucial for consistency; agencies develop multilingual term bases validated by subject matter experts.
By documenting cultural references and their significance, agencies help preserve traditional knowledge and ensure culturally appropriate translations.
Their work in these areas helps to preserve endangered languages, enable digital inclusion, support economic development, maintain cultural heritage, and foster global understanding.
Working with low-resource languages represents both a technological hurdle and an opportunity for innovation in the language industry. While AI and machine learning continue to advance, successfully handling LRLs requires a balanced approach that combines technology with human expertise.
At MarsTranslation, we understand that preserving and processing low-resource languages isn't just about overcoming technical challenges – it's about protecting cultural heritage and enabling global communication for all communities.
Our comprehensive approach combines advanced NLP technologies with native linguists who understand deep cultural nuances, ensuring quality and authenticity in every project. Whether you need Bengali translation, Malay translation or translation in any other low resource languages, we have native experts to help you get fast and flawless results.
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