Lingvanex Translator
Ultimate Machine Translation & Speech Recognition
In March 2017, we found a project called Open NMT — a joint development of Systran, one of the leaders in the machine translation market, and Harvard University. The project was just launched and offered translation based on new technology — neural networks.
We took OpenNMT as a basis. This happened in early 2017 when it wasn’t perfect and contained only basic functions, purely for academic research. A lot of bugs were found, everything worked slowly, unstably and crashed under light loads. In our group chat, we all tested together, tracked errors, shared ideas, gradually increasing stability (then there were about 100 of us). At first, I wondered: how is it so, why does Systran grow its competitors? But over time, I understood the rules of the game, when more and more companies began to publish their developments for processing natural language in open source.
In 2016, we found several opensource projects — Apertium, Joshua and Moses. It was a statistical machine translation suitable for simple texts. From 3 to 40 people supported these projects. Later it became clear that we need powerful servers and high-quality datasets which are expensive. Even after we spent money on hardware and a quality dataset for one of the translation pairs, the quality left much to be desired.
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