HELIX - Leveraging Hybrid AI Models for Effective Zero-Corpus Machine Translation.

Machine Translation (MT) has seen significant advancements with the rise of statistical models and neural networks. However, translating into a zero-corpus target language—one for which there are no available digital resources like parallel corpus, dictionaries, etc—remains a substantial challenge. In working with many of our clients who serve the minority language communities (a group of people who speak a language that is different from the majority of the population in their area or state) we realized that they would never have access to knowledge that is well documented in many languages. A substantial number of them didn't even have a writing system. Thus, the digital corpus needed for existing large language models was a distant dream. Their need to develop a writing system also gave us the inspiration to create a hybrid AI model combining rule-based and statistical machine translation approaches. By integrating the strengths of both methodologies, our AI engine, HELIX delivers an accurate translation in digitally low-resource languages, leveraging linguistic rules alongside data-driven statistical methods. In extensive community focus group subjective testing using the bilingual evaluation of machine and manual translation, the community failed to distinguish between the two. Many even preferred the machine version which was natural in its expression while consistent in its word and syntactic usage.

Machine Translation has progressed through various phases, from rule-based systems to statistical models and neural machine translation (NMT). Each approach has its strengths, but translating into a zero-corpus language presents unique difficulties. Zero-corpus languages lack parallel corpora, making conventional data-driven methods less effective. Thus, a hybrid AI synergies rule-based and statistical machine translation techniques to tackle these challenges. The Rule-Based Translation components handle the basic grammatical and syntactic rules of the target language. It generates initial translations based on linguistic rules and predefined dictionaries. The Statistical components refine the rule-based output using statistical methods. It incorporates a small amount of data to refine probabilities before generating a machine output. adjust and enhance translations.

Rule-based MT relies on linguistic rules and dictionaries to perform translation. In all cases the minority languages adopted the orthography based on the sound patterns with some divergences of the nearest language of wider communication (LWC). HELIX begins with the identification of convergences and divergences written in rule form. Identification of Convergences and Divergences plays a crucial role in Helix MT development. This is achieved using a questionnaire which has a dynamic set of questions. Then using human post editing pattern tracers, flexibility and adaptability is built into HELIX to use statistical models to evaluate the subset of the head meanings in sentence phrases.

The key to this evolution of HELIX was the active collaboration of the community that supplied the words and sentence forms that led to better semantic transference. Within a short span of 10 months HELIX was able to generate machine outputs that were widely understood. The accuracy was further enhanced with clarity in the post editing tracers incorporated into the dictionaries. The outputs were evaluated by the mother tongue speakers who dealt with it individually, in focus groups and community gatherings. Developing the machine in the midst of the community helped in their acceptance and ownership. HELIX is capable of being as natural or formal in expression depending on the literacy levels of the community. The community translation teams who are bilingual eventually take over the management and updating of the libraries and dictionaries thereby making them the mother tongue machine translators.

According to Internet sources there are about 7151 living languages in the world of which over 90% or around 6435 languages are minority languages. The world's top 4 languages of English (1,456 million speakers), Mandarin (1,138 million speakers), Hindi (610 million speakers) and Spanish (559 million speakers) constitute approximately 48.5% of the world's population. Therefore, a lot of minority language communities remain linguistically marginalized with no access to benefits that can be availed by correspondence or filling forms (offline and online) in their closest mainstream language. Being a spoken language in this context can be further alienating. How can all of these people be a part of this increasing small world supposedly shrinking with information technology? All emerging opportunities and technological advancements remain elusive to these communities. Their children lack opportunities since most of these languages are found in the remote and backward regions of Asia, Africa and South America where the schooling system is in their mainstream language. HELIX can help in integrating these communities into mainstream society. With no clear commercial benefits this remains a task for the government and charitable organizations to undertake as entire generations pass away ignorant of the world that exists beyond their linguistic or sometimes even their geographical borders.