Technology Features & Specifications
This tool is created with the state of the art deep learning architecture (both seq2seq and transformer-net), and it’s standard open-source implementation (OpenNMT-py). The models are trained with 3 NVIDIA GTX-1080ti GPUs. An online demo model is launched in our on-site network. A more sophisticated offline tool is created with the dot net framework.
Since we train our NMT system on a corpus that comprises texts and conversations from many different domains, it can be applied to various translation settings.
(a) Conversation translation: The tool can be used to translate utterances in our day-to-day conversations.
(b) Government projects: In various government projects this tool can help to translate standard documents.
(c) Military & defence: This tool can help to extract information that is valuable for defence (e.g., terrorism).
(d) Health: This tool can be used to translate medical/health related texts. However, we may need to tune the system for that.
(e) E-commerce: Communicating with various customers in their native languages may increase the feasibility and usability of the system.
(f) Software & technology: In the software industry it is a good practice to deliver the GUI in a different language for the different region. This tools can be used to do such translation.
Market Trends and Opportunities
Machine translation is now a growing industry. Most leading tech companies such as Google, Microsoft, Facebook, Amazon, Yandex, and Baidu have their own home-grown MT systems. However, these companies do not focus explicitly on Malay-English, where we see our opportunity. Malay is the official language in Malaysia with a population of 31.19M. It is also one of the official languages in Singapore and Brunei. Therefore, the translation tool can have a significant impact in this region for personal and commercial (trade, law, business) purposes.
Customer benefits involve the wide range of application scenarios mentioned above. Our tool is good at translating conversational data. Since in our research work, we focus explicitly on low-resource languages (Malay, Indonesian), the tool should be better than the ‘one-hat-for-everything’ Google translator. For special needs (e.g., to tune the model for a specific application), the authors can support with their expertise and knowledge.