There is a running joke in the translation industry that machine translation will be a solved problem in 5 years. This has been updated every 5 years since the 1950’s. The promise was always there, but the technology of the day simply could not deliver.
However, there has undoubtedly been progress. When we started our company over 10 years ago, MT was considered a joke and ridiculed. At the time, we looked to purchase MT technology, but were unable to find any technology that could rise to the challenges that we needed to address. At the time we had the formidable task of translating all of Wikipedia and other high volume and very mixed content domains from English into mostly second or third-tier Asian languages.
To find a way to address that problem, we flew in Philipp Koehn to introduce him to our concepts and ideas. Philipp at the time was a promising researcher in Statistical Machine Translation (SMT) and recently created a large corpus of bilingual content derived from European Parliament documents and had just released the first version of the Moses SMT decoder which in the meantime has become the de-facto platform for SMT. Soon after, Philipp joined our team as Chief Scientist and has been driving research and development efforts ever since. Philipp joining the team gave us the in-depth knowledge of MT that few had at the time and allowed us to stay ahead of the competition from a technical perspective. While Philipp was the father of the Moses decoder, his Master’s thesis in the 1990’s was on Neural Networks before they become practical for machine translation.
The hype about Neural Machine Translation (NMT) and Artificial Intelligence (AI) has hit a new peak in the last year, with claims from Google and others that translations produced by their MT technology is difficult to distinguish between human translation. This has raised eyebrows, with some impressed, some in laughter and as expected a lot of skepticism. Riding on the wave of attention and hype surround NMT and AI are a number of companies that claim to deliver products or solutions that leverage the latest and greatest approaches, some of whom are established and some new to the space. One of the more recent launches has attempted ride the hype wave by claiming to beat Google and many other large number claims that to most sound great, but lack any real meaning once you dig a little deeper and understand more fully the claims. In making such claims, the next wave of hype that MT is so often criticized for has been has reignited, and perhaps even relaunched. The next wave of hype is going to be deep learning AI and in the case of machine translation that is known as Deep NMT. Today there are a handful of MT vendors, such as Omniscien Technologies, who are delivering Deep NMT commercially as compared to those offering NMT. Expect there to be some confusion around the difference between NMT and Deep NMT for some time.
But before we get too excited about claims of great quality and all the other usual types of marketing and counter statements that go along with each wave of MT quality hype, let’s take a more pragmatic view of MT and how to read the hype, understand the issues and ask the right questions.
Read more by downloading our white paper “Translating the Machine Translation Hype”