What you describe in your second comment sounds much more reasonable than claiming things like double the Thai content on the web by the end of the month. The first comment reads like a stump speech for investors.

My only beef is with outlandish claims (research or no research). I’m all for crowdsourcing, and I contribute to both Thai and English Wikipedias. If your project actually results in more high quality content, that’s great. I’ve signed up on your site so I can follow what your company is doing more closely.

The data that we gathered on content by language was for a point in time in 2007. Also our focus was SE Asia excluding India. The research was validated by Gartner group colleagues and by the leading ISPs in the region who shared information with us. We also crawled much of the web in the region for local language content including all the sites you mention, so we feel that the estimates we came up with are reasonable and accurate in general. While there may be rounding errors, in general we stand by our research, but would be happy to change our assertions should a more credible data source be made available. It is true that the region is growing rapidly in terms of online population and content but the basic fact remains there is MUCH LESS content in the languages we are targeting.

In fact, you can go to the Wikipedia today and see that the Thai Wikipedia has about 40,000 articles versus 2,750,000 article in the English Wikipedia. If you look closer you will also see that the articles in the Thai wiki are much shorter and limited in scope. This would put a Thai student at a relative disadvantage in terms of information access. By translating the bulk of the English Wiki we will significantly more than double the volume of content on wiki related material available to monolingual Thai speakers.

We are very much aware that what we produce is not equal to human translation and sometimes can justly even be called crap. Thus we employ 300 part time translators who go through early versions of the MT and correct errors which are fed back into the system to raise the quality. This is done in a way to maximize the benefits of the corrections. As you point out it is still very likely to have errors (even though you did notice it improved) and will still need to be corrected, but the examples you point out are already useful to many people who are not professional translators. Our objective is to build a community of people who care about the content to come and help us “clean up” the errors and our technology platform is designed to collect these correction to help improve the ongoing and future automated translations. In case you have not noticed, this crowdsourcing effect is working in many other arenas. The Wikipedia itself is something that Encyclopedia Britannica dismissed as crap not so long ago. The Wikipedia is far from perfect but volunteer contributors have produced a resource that is today one of the top ten websites in the world with about 2M unique visitors per month and still growing.

Our intent is to get the quality to a point where a crowdsourcing engagement becomes possible, since volunteers will be much more likely to make small error correction contributions than completely translate a document. If you take a closer look at our Thai portal you will see that we ask people to join us to clean up our initially “crappy” translations. This effort is just beginning and we expect that it will help raise the quality to levels where there are few or no issues in terms accuracy and meaning even though we may still fall short of professional translator standards. We already have evidence that this approach works and invite you to monitor the site to see the content quality continue to rise. In time we think it will be more than compelling.

Already millions of people use MSN Live, Google and Babelfish to translate content they would not otherwise be able to access each and every day. Data indicates that many find this useful though it be gobbledygook sometimes. Ours is a more focused effort, that attempts to reach for significantly higher quality. This can only be done by engaging humans, millions of humans if possible. The internet and social networking trends increasingly support this type of collaboration. We know that while there may be 500,000+ professional translators in the world, there are probably hundreds of millions who are competently bilingual who may lend an occasional hand to correct raw MT. Our mission is to engage a tiny portion of these people. If we are successful we will at least double the useful web content that exists in these countries today even though along the way it may sometimes look wanting and useless.

Here is a sample of French to English, which would help me if I got stuck:

In less than 25 years, the State of Quebec had restructured, modernized and developed in depth. Education at all levels cover the entire territory, health institutions were established in all major centers, Crown corporations multiplied, all major public services (police, roads, energy, local government) were assured everywhere. Failing to be independent, the State of Quebec became a French province, resulting in a new cultural revolution marked a transformation of the business and industry, environment hitherto traditionally anglophone and daunting to the language of the majority. In addition, Quebec has also promoted the development of bilingualism in the federal state.

I agree. Many companies have a simple choice:
* Continue to write ‘fluffy’ text, and pay translators to translate the text.
* Write text that is clear for a machine to ‘understand’ and use machine translation.

Sometimes, machine translation is the best option for a company. Sometimes, human translation is the best option for a company. A 5-step flow chart on http://www.international-english.co.uk/mt-or-human-translation.html shows how to choose the best option.

“The work we are doing on the Wikipedia results in doubling the size of the Thai web by end of February.”

This seems preposterous. The pie chart on the Asia Online page you linked claims that all other Asian languages (not Chinese, Japanese, or Korean) combined make up only 10M pages on the internet. That’s pages, not sites.

But there are many Thai websites in the million + page range each — the big discussion board sites. Sanook.com, Pantip.com, Kapook.com. And plenty more not far behind. And this is one language. How are you counting?

Saying you’ll double the number of Thai pages on the internet at all, let alone by the end of this month (48-hour warning) … I don’t know what to say. It sounds good to clueless investors, I guess.

As it happens, I saw (through a friend) some of the in-progress output of your company’s work from late last year. Two versions of your Thai translation of the Wikipedia article on Harry Potter. The earlier one was gobbledygook, about what we’d expect Google Translate to produce. The later one was much improved — it must have had a lot of human input. But even then it contained simple errors like Newark (the city in New Jersey) translated as นิวยอร์ค (“New York”), and $15 billion translated as 15 ดอลลาร์ล้าน (“15 dollars billion”). Those are the ones that stand out in my mind. And the rest was still often unintelligible overall.

Even if you did meet your project’s goal, that would mean that thanks to your efforts, half of the Thai content on the internet would be unnatural, non-native, and semi-incoherent. I don’t think that’s particularly valuable.

I have no beef with what your company is doing in theory, but please try to be more realistic about your goals, and the quality of what you can produce.

I also work with Japanese and Chinese, and there have been large gains with Japanese/English over the past few years.
Try going to a Japanese news site and running it through Google Translate. Still not good enough in most cases to edit, but the news articles were not pre edited, and the program is free. $0.00.

Computer speed keeps doubling every 16 months, so we will see much better results in a few years. Even most Japanese/English will become editing work.

The professor in question was Martin Kay (Stanford), and I’m pretty confident that he knew what he was talking about

I promoted combined Controlled Language and MT for several years in contexts in which it was possible to refine the source texts. Then I found myself in other contexts on the receiving end with no opportunity to change the source. It was necessary to create adapted MT techniques without “Controlled Language” by emphasizing the terminology extraction and rapid terminology entry (with ability to handle variation) into commercial MT desktop packages, by optimizing a methodology with nothing more than MS Word and MS Excel + the MT software program. Nothing different that any professional translator would have at their desk.

“Controlled Language” covers a lot of different levels ranging from something as basic as terminology management, to grammatical rule standardization, and then even semantic domain modeling. It all comes down to being able to improving authoring and translation, and there are lots of style guides out there on International (technical) communication. Caterpillar had their controlled language guide with 120+ rules, General Motors with their CASL guide of 60+ rules, etc Sharon OBrien did her PhD thesis a few years ago to compare several different controlled language guides and find the overlapping between them. General Motors did however create a basic version based a dozen simple rules for improving international communication, and which leads to better translatability.

See the following site which gathers all of the primary set of info on controlled language based on the series of international workshops on the topic (combined with MT and translation)
http://www.geocities.com/controlledlanguage/

If a software checking application, specifically based on the writing rules, is not implemented, then it is very difficult to maintain the consistency over time. That the difference between having guidelines written in a document, and then having writing rules which are implemented in a software program that flags the authors every time a writing rule or terminology entry is not followed how it is expected.

I’ve done a lot in providing detailed descriptions of MT projects, platforms used, context of software and hardware, time constraints, type of task and amount of work done in specified time periods, and provided empirical data on it.
See the sets of MT project case studies at:
http://www.geocities.com/mtpostediting/

And don’t use the copy/paste idea with Google. There are much better ways of doing it. Read those project case studies which achieve a much higher volume and with quality that is deemed not just publishable, but in one case study it was for a critical customer project for a company. It brought back more business.