As a low-resource language, Burmese is a language that is native to Myanmar and parts of Bangladesh, India, China, and Thailand. There are around 33 million people who speak it is a native language and around 10 million speak it as a second language. It comes from the Sino-Tibetan, Lolo-Burmese language family. <\/p>\n
When it comes to translating Burmese, this can be a very challenging task for linguists, translators, and localization specialists because it forms a part of the low-resource languages for NLP (Natural Language Processing). This can pose significant obstacles in translation processes and with this in mind, we decided to share some of our experiences working with Burmese as a low-resource language. Take a look at our shared findings below.<\/p>\n
In short, a low-resource language is a language that faces challenges in translations and localization processes because there is not enough data to input into an NLP system to get more accurate translations at a higher percentage of the time a translation needs to be done. NLP is a system of translation that gathers as much data as possible for a source language and then uses this data to translate the source language into a target language. However, when there is too little data on a low-resource language, what often ends up happening is poor-quality translations with many mistakes that an artificial intelligence (AI) program dealing with translation often cannot pick up on.
\nThis is why expertise from human translators is so crucial in this process as the NLP for low-resource languages often lacks a large enough data set to process accurate translations. Examples of high-resource languages are French, English, and Chinese, whereas Burmese is a low-resource language because there is not much data to support quality translations.<\/p>\n
Burmese is considered a low-resource language because there has not been sufficient effort put into building a strong NLP for low-resource languages. These languages are spoken by fewer people, there is less demand for these translations, and as such, AI and NLP databases do not have sufficient information about the language to process and produce accurate translations. This is why many errors crop up in the process of translating from and to Burmese.<\/p>\n
At 1-StopAsia, we have first-hand experience with translating Burmese. However, at the outset, we noticed a couple of errors that had cropped up in the process of translating this language. It was not only that Burmese is one of the low-resource languages for NLP but also that this situation led to a less-than-optimal solution for several of our clients.<\/p>\n
![\"Enhancing](\"https:\/\/www.1stopasia.com\/blog\/wp-content\/uploads\/2023\/05\/Fixing-a-Low-Reource-Languages-Quality-Issues-Burmese-300x178.jpg\")
Because quality assurance is critical to our promise to our clients, we embarked on a way to resolve this issue. Two of the most common issues that we identified in Burmese translations were spelling errors and mistranslations by the linguists.<\/p>\n
To address this issue, we undertook several important qualitative steps and created action plans to ensure that the translations were of high quality. For example, regarding the misspelled words, we created glossaries that were approved by the clients that we would build into our database of low-resource NLP for Burmese.<\/p>\n
We also ensured that any mistranslations were sent back to the relevant linguists and we also expanded our talent pool of linguists for Burmese so that we would have higher-quality translation output going forward.
\nUltimately, this resolved the client\u2019s issues and we ended up with a favorable solution for future projects that involve Burmese translation by ensuring that our existing low-resource NLP was boosted both on the AI side through glossaries and pre-defined terms and on the human side, but expanding and strengthening the quality of our Burmese translation team.<\/p>\n
To ensure that we consistently deliver high-quality translations in Burmese to each of our clients, we take important and critical steps to identify translation issues as and when they arise and to develop action plans to ensure that issues are not repeated in the future. We also strive to build onto our NLP database for Burmese so that it transforms from a low-resource to a high-resource language data set. This ensures continued quality assurance from our team as we factor in both the technological and human elements of Burmese translations to give every client needing such a translation the highest quality output that they can expect.<\/p>\n