Last week, Language Weaver projected a US$67.5 billion market for digital translation, enabled by advances in machine translation (MT). For the last few years, we have released an annual estimate of the market for outsourced translation, localization, and interpretation. For 2008, human-delivered translation activities will total a hefty US$14.25 billion (see our “Ranking of Top 25 Translation Agencies“). On the software side, we estimate that the MT software market falls well short of US$100 million. Added together, there’s a lot of daylight between our numbers and Language Weaver’s estimate. Where’s the disconnect? Over the last week, we’ve spent a lot of time talking with various people about the US$67.5 billion projection.

Let’s start off by deconstructing the 67 billion dollar number. That is an estimate of the monetary value that Language Weaver thinks MT suppliers “could” translate for corporations and governments; the operative phrase in the company’s press release is “untapped markets” where automated translation could increase the volume and lower the cost of human translation, which stands at current market prices of 10-40 cents per word.

How good is Language Weaver’s sizing of the as yet unrealized market? We think its number is way too low, especially as the amount of stored content grows at record levels (see the figure below from our report on “Automated Translation Technology“).

The untapped market potential is much higher, but the problem is still getting buyers on board. Language Weaver will target customer care, business intelligence, and user-generated content, three markets where companies could benefit from moving content out of linguistic silos. However, the organizations today that stand to gain the most from MT are those driving advertisement-reading eyeballs to their sites. The challenge that Language Weaver and rival developers face is getting more people accustomed to the idea of paying for MT software or SaaS solutions that will help them translate their content into other languages. Three roadblocks stand in the way:

• Free machine translation obscures the value.  There’s an enormous amount of content that’s translated every day online using free online machine translation sites, but no one has figured out how to directly monetize those interactions. We have long contended that there’s far more text that consumers, businesses, and governments might run through those engines if they could more easily plug them into workflows, e-email systems, mobile phones, and other networked appliances. Combine a dollar figure for the unmonetized activity that’s happening today at sites like Google Translate or Yahoo!’s Babel Fish with the dollar value for things that should be translated - and you’ve got some really big piles of zeroes. The problem is that there are usually no positive integers to the left of those zeroes. Bottom line: Too much of it is free.
• Unpaid human translation appears to be a panacea.  Another rival to MT is community or collaborative translation for both company- and user-generated content, such as we’re seeing at Facebook (social networking), Livemocha (language learning), and NetBeans (Java software development). These communities can fill some of the demand, but nowhere near all of it. That leaves a lot of information forever locked in the language in which it was created.
• An uneducated market expects too much or too little. Potential buyers retain unrealistic (read “Star Trek” or Hitchhiker’s Guide”) expectations of what they will get out of machine translation. Some ignore the quality issue altogether, posting babble-fishy output and thinking they did a good thing in providing any in-language content at all. Meanwhile, many individual translators and too many translation agencies miss the point; they think that MT threatens their livelihood rather than viewing it as a productivity enhancer.

That said, the corporate and governmental sectors may be turning the corner vis-à-vis MT acceptance, if not purchasing. A poll conducted by the International Association for Machine Translation (IAMT) and Association for Machine Translation Americas (AMTA) for SDL, another provider of machine translation technology, found that 40 percent of the 385 surveyed individuals were “now” likely to use MT. Of those roughly 150 receptive respondents, 62 percent said they would use it for technical documentation, 49 percent for support and knowledge-based content. That’s good news for the MT software sector, but could be bad news if automated translation merely displaces the work of traditional translation agencies rather than increase the size of the overall business.