Forthcoming Publications

In my post on September 28, I mentioned that Şebnem Susam-Sarajeva of the University of Edinburgh (see photo) was hunting for fan translations of song lyrics. She’s still hunting. But in the meantime, she’s now become co-editor of a new book with the title Non-professionals Translating and Interpreting: Participatory and Engaged Perspectives, to be published by St. Jerome Publishing of Manchester, UK. At present it’s at the preliminary stage of a ‘call for papers’, and publication is not expected until June 2112.

The call says:

This collection [of articles] proposes to explore the field of non‐professional translation and interpreting with a view to learning from the individuals who take on translation/interpreting activities; the networks and organisations for which they translate and interpret; the media which facilitate the distribution of amateur translations; and, last but not least, the societies where these activities emerge and impact on the political, economic and cultural spheres.

The themes are:

• Amateur news translation and distribution
• Non‐professional translation/interpreting within the context of religion
• Scanlation and fansubbing
• Fanfiction and translation
• Translation and the blogosphere
• Interpreting within local NGO settings
• Non‐professionals translating/interpreting within conflict situations
• Activist translation/interpreting
• Amateur translation as a form of cyberactivism
• Child‐language brokering vis‐à‐vis professional interpreting

The contributions should be between 6,000 and 10,000 words and the deadline for submission of abstracts is July 30.

Hard on the heels of this announcement comes another Call for Papers: Community Translation: Translation as a Social Activity and Its Possible Consequences. The editor is Minako O’Hagan of Dublin City University, and it’s to appear in Belgium in the Linguistica Antverpiensia series. It’s perhaps more limited in scope than the Manchester book, since it seems to be entirely devoted to crowdsourcing.

[It] seeks to address how far-reaching the consequences of the new trends afforded by new technological platforms may be, possibly affecting many different dimensions of translation.

In this case the deadline for proposals is June 20 and the publication date has been set at December 2011.

Both of the above are academic publications – that’s why it takes so long to bring them out. They have to be ‘refereed’. But some of you out there are academics, so this is a chance for you to be published and get in on a new field before it becomes crowded. Two volumes do not a literature make, but they’re a welcome sign of stirring in academia.

CONTACTS
Şebnem Susam-Sarajeva: s.susam‐sarajeva@ed.ac.uk

Minako O'Hagan: minako.ohagan@dcu.ie

Photo: University of Edinburgh

Usable Translation Postscript

In my April 14 post, I used the term usable translation’. I should have mentioned that it occurs, along with a lengthy discussion of it, in a book by an experienced professional Canadian Government translator and teacher at the University of Ottawa. Here’s the reference:

Malcolm Williams. Translation Quality Assessment: An Argumentation-Centred Approach. Ottawa: University of Ottawa Press, 2004.

There’s a preview of it on Google Books.

A Child Native Translator

I’ve written about child translators, now here’s a concrete example. It’s an English to Spanish translation by a child Native Translator. The above is one page from a nine-page book of it.

The translator – let’s call her Maria or M – was nine years old. (She’s now ten.) She lives with her family near Valencia and goes to the village school. Her first language is Spanish. Like all Valencian children, she’d had English classes at school from first grade onwards. In addition, she was taking a weekly private English lesson for reinforcement and for conversation practice. She’s bright and eager to learn. The piece of translation shown here is both an accurate translation of the English and correct Spanish. That’s good, but the English is very simple, so it’s not surprising for a child of her age and schooling. Furthermore she had the help of the picture for her COMAL. But it’s not a word-for-word translation; the Spanish is idiomatic: se meten en for go into and no estan for are not there.

There are several things about this translator that are particularly interesting.

1. M’s critical remark written above her first clause: no hace falta (it’s not necessary). She wrote this because the information in the clause isn’t expressed in the English, though it’s implied in the development of the story. So she added something to the source text, apparently thought better of it when comparing it with the English, and corrected. This is an indication of her metatranslational awareness.

There are several other places in the book where she corrected herself.

2. M wasn’t asked to do the translation. She proudly produced it to her teacher. So there was no elicitation and no communicative need for it. Perhaps she had an ulterior motive in a desire to please her teacher, or she may have done it to prove she could, or she may just have done it for amusement (ludic translation). Anyway, it would be quite wrong to think that all child translation is of the Language Brokering kind.

3. M was a Native and not a Natural Translator (see last year’s July 13 post for the difference or use the Search box to ask for Essential Definitions). I say this for three reasons:

a) M lives in a bilingual community where translating is going on all around between Spanish and Valencian. It’s virtually impossible to grow up here without being aware from an early age that people speak two languages and without hearing and seeing translations.

b) The method and the books used for teaching English in M’s school and in her private lessons avoid translation. Nevertheless, the reality is that teachers often have recourse to it for explanations and equivalences.

c) M used a dictionary when she had a vocabulary problem. We know it because she said so. She had a bilingual dictionary and she knew how to use it. Translating may come naturally, but knowing how to use a dictionary does not. (I’ll have more to say about this in a later post.) So using a dictionary is a sure sign that somebody is a Native and no longer a Natural translator, even if they’re still at an early stage.

Thank you, Followers

Now that the number of declared Followers of this blog has passed the 50 mark, it’s a good moment to thank you for your interest and fidelity. I know too from correspondence that there are many other occasional readers.

When a new icon appears in the Followers panel, I always look to see whose it is. So I know something about some of you, but many of you haven’t posted a Profile. Never mind. I’d like you all to know that you’re a great encouragement.

Machine and Human Translation 5: Self-help

In the report of the BBC's experiment in conferencing with MT (March 24 post), there’s an example given of the output from Google Translate:

Dowry Allowathb of Khartoum, Sudan, submitted a comment in Arabic on the topic "If you could say one thing to the world, what would it be?" which came out in English as:
That the budget of one war enough to satisfy the hungry Africa, not to mention the budget arm of one of the major powers.

“Not perfect, but intelligible,” is the reporter‘s verdict. Is it intelligible to you? Here‘s a simple test: rewrite it in standard English, or in standard whatever your language happens to be. What you already know about the state of the world will help as a referent for your COMAL.

As the reporter says, a proposition doesn’t have to be perfectly stated for it to be intelligible. I call this patching-up process reader/listener input tolerance, or reader self-help for short. The ability of readers —and listeners for that matter— to make sense of imperfectly formed input is not peculiar to translations. We do it all the time in our own languages. The sense we arrive at may sometimes be wrong, but we strive for it and we do so instinctively. Of course it’s better if the translation is well expressed and we don’t have to strive so hard, but faute de mieux…

Here’s another story about self-help.

Back around 1990, a branch of the Canadian government was using MT to translate into French the notices of job vacancies that were posted up each day in government employment offices. The MT system was rather primitive, and at one point it became notorious for a classic mistake. The French for man is homme. But Man. (with the initial capital and the dot) is also a standard abbreviation for Manitoba, one of the Canadian provinces. So when there was a job vacant in a town in that province, the location would come out, for example, as Winnipeg, Homme. One day I ran into a senior official from the ministry and asked him about this. He replied,

“The only people who complain about our translations are university professors like you. Our clients are happy with them because they get them the same day. If they had to wait even 24 hours while we sent them to the government translation bureau, the chances are that the vacancy would already be filled. And as for Manitoba / Homme, well they soon learn that Homme means Manitoba.”

That brings us up against two other phenomena of input tolerance. The first is that if a mistranslation is regular, readers who encounter it often will probably learn from its context what it really means. The second is that motivation plays a role. The more people need something, the more accommodating they prepared to be are in order to get it.

Yet another reason people may be tolerant of faulty MT is that they don’t really want a translation in the normal sense. All they want is to get an idea, the gist, of what a document or speech is about. For documents, this is called scanning. The earliest successful large-scale MT system was SYSTRAN, which became operational around 1970. Nowadays anyone can buy it and it’s on the internet, but at first its sole client was the Foreign Technology Division of the United States Air Force. The USAF used it for scanning Russian technical literature after the Sputnik awakening. The purpose wasn’t to produce usable translations but to help identify texts that might be worth closer attention. And then the translating was done by human experts.

To sum up, MT so far, and with rare exceptions, demands special reading skills that are still the preserve of humans.


REFERENCES

Dave Lee. BBC debate demonstrates power of machine translation. BBC News, March 18, 2010. http://news.bbc.co.uk/2/hi/technology/8575526.stm.

Walter Daelemans and Véronique Hoste (eds.). Evaluation of Translation Technology. University Press Antwerp (Belgium), 2009. 262 p. 35 euro. Just out! This one is for specialists.

SYSTRAN. http://www.systran.co.uk.


Peter Toma, the Hungarian-American founder of SYSTRAN. I met him briefly when he came to Ottawa to help try and sell SYSTRAN to the Canadian government. The photo comes from a very interesting website about early computers, http://tjsawyer.com/B205Home.htm.

Machine and Human Translation 4: Usable Translations

A contributor commented on one of my earlier posts as follows:

I don’t think we are looking for a 100% accuracy when we use MT software. If you reduce the effectiveness, not as low as 80%, but not so high as a human translator, I think the MT is useful enough for performing interesting tasks.

It depends what the translations are used for. You would certainly want better than, say, 90% accuracy (i.e., 10% wrong) in a legally binding contract, or an aircraft maintenance manual, or a doctor’s prescription or in court interpreting.

Talking of medical prescriptions, consider the following.

Sharif and Tse, who is with Dartmouth College in Hanover, New Hampshire, surveyed 286 pharmacies in the Bronx, New York — where 44 percent of the population speaks Spanish — about whether they provided medicine labels in Spanish to their customers who needed them. About three-quarters did so. Among these pharmacies, nearly 90 percent used computers to translate labels from English into Spanish, 11 percent used staff members, and 3 percent used professional interpreters.
Sharif and Tse then looked at 76 medicine labels they had generated using 13 of the 14 computer programs pharmacists reported using for translation.
They found that half of all the labels contained serious mistakes. Thirty-two of the labels included incomplete translations and six contained major spelling or grammatical errors.
Computer translation programs can clearly be improved, Sharif said, but this doesn't mean a human being shouldn't be checking the computer's work. Ideally, she added, pharmacies should have professional translators on staff to ensure that labels are being translated properly. “Figuring out how to pay for this,” Sharif said, “is probably something that belongs within the health reform conversations."

There Sharif has hit the nail on the head. Who’s going to pay for it? Professional Translators with pharmaceutical expertise are specialists, and they expect to be paid accordingly.

However, there’s the other side to MT. A Chinese student who came to Spain wrote to me recently that although she knows no Spanish, she had been able to obtain the information she needed for her trip and make her local bookings thanks to the MT translations of web pages.

So let’s take it for granted that MT has its uses. The question remains: how good does the translating need to be for it to be used at all? Or to put it the other way round, at what level does it become so bad that it’s useless? It’s a question that concerns not only MT. Many human translations are far from perfect, and senior Expert Translators who correct and improve translations (they’re called Revisers or Editors in professional circles) will tell you that some are so bad, it would take them less time to do a fresh translation from the original than to patch up the bad one. It’s a subject that lacks proper study. Almost all the literature I’ve read is concerned with good translation and how to achieve it, and preaches openly or by implication that the bad ones should be consigned to the rubbish basket. I once thought of writing a counter-article with as title In Praise of Bad Translations, but I haven’t gotten around to it.

The usefulness of imperfect translation is a very practical matter, not least because there’s so much of it. As noted above, perfect or near-perfect translation is expensive. It’s also very labour intensive. There comes a point of diminishing return beyond which more time and money spent on a translation may not be worth it.

And last but not least, it’s a matter for concern in research on Natural Translators, since we expect that a significant part of their production will contain mistakes.

So let’s admit another standard besides the traditional good translation / bad translation one. It’s the usable translation / unusable translation threshold. To which must always be added the qualifiers, usable when, where, for whom and for what?

More to come.

REFERENCES
Anne Harding. Drug label accuracy getting lost in translation. Reuters U.S. Edition. New York, April 9, 2010. http://www.reuters.com/article/idUSTRE63853K20100409.

Iman Sharif and Julia Tse. Accuracy of computer-generated, Spanish-language medicine labels. Pediatrics, April 5, 2010. http://pediatrics.aappublications.org/cgi/content/abstract/peds.2009-2530v1.

Church Interpreting, a Gift of the Spirit

I’ve written several times about church interpreters (July 29; August 3, 11 and 27; October 28), and I know from the comments and the emails I’ve received that several of you have a keen personal interest in it.

Now comes a splendid report that describes what must surely be the ultimate in church interpreting organization, or indeed in any kind of interpreting organization. It’s about the interpreters for the annual general conferences of the Mormon church (The Church of the Latter-day Saints), known for short as Conference. Most of the work is done in simultaneous at the LDS Conference Center in Salt Lake City, but some is done at remote sites through a tie line telephone network and some even more remotely.

”Conference is interpreted live in up to 92 languages — 52 in the conference center, 28 via the remote Tieline system and another dozen on-site in countries across the world… In all it takes 800 people, including hundreds of interpreters and dozens of support staff, for Conference.”

The scale of all this reveals two things about the Mormon church. The first, and more obvious, is how widespread it has grown to be in the world: from Utah to Tonga, and not forgetting Valencia. The second is something that derives from the Mormons’ fulfilment of the New Testament exhortation, "Go ye therefore, and teach all nations..." At any one time, they have 50,000 full-time missionaries worldwide, most of them single young men and women in their late teens and early twenties, and they’re assigned to places that are usually far from home. What’s more, they’re enjoined to learn the languages of the places they’re assigned to, and in the two years duration of their missions they have the time to do so. As a result, the leading Mormon campus, Brigham Young University at Provo, Utah, is an extraordinary reservoir of fluent bilinguals, many of them speaking languages that are little known or studied elsewhere. (And I think it’s no accident that Brigham Young also has one of the pioneers of computer-assisted translation, Alan Melby,)

Formal training for the Conference interpreters is minimal:

“For each language, a language team coordinator handles finding and recruiting interpreters for those languages… Through pre-screening their language ability and their standing in the church, they are then brought in for a three-hour orientation.”

However, many of the interpreters have been at it for a long time.

”Diana R. Tucker… has been interpreting in Spanish for conference for more than 40 years.”

That too is a record that’s hard to beat. Whatever they were when they started out, it’s certain that such people have risen by experience to the level of Expert Translators within their specialized domain. Some of their remarks show that experience:

"One has to practice their trade all the time," says a Tongan interpreter. "Everyone has their own way of preparing for Conference… It isn't that we just show up Saturday morning and sit down and things flow… but there is always some nervous energy [needed],” He was just as nervous this weekend as he was interpreting for the first Conference. "I pray that I will be equal to the task to provide the right amount of expertise in the delivery of the talk.”

There are many interesting technical and historical details in the article I‘m drawing from. I’ve observed before that church interpreters are not inhibited by the convention of neutrality and detachedness that conference and court interpreters are supposed to maintain. At LDS,

”Some sit to interpret. Others stand using music stands to replicate being at a podium. Some are still and others are animated as they interpret… They also try to match the emotion and tone of the speaker as appropriate for the talk and the language… Our intent it is to try to within the culture, to mirror the speaker and his style."

I’m not religious. But I recognize that religious people often speak a different ‘sublanguage’ from me, and that I have to adapt my thinking to it if I want to understand them. As you know, I believe there’s a fundamental competence in or underlying translating and interpreting which isn’t acquired by teaching; it comes with us and is awakened by or is applied to bilingualism. I used to say to my interpretation students, “I’m not your teacher, I’m your coach.” (Some of them liked that approach, some didn’t.) The translation division director at the LDS Conference Center is Jeff Bateson. He says,

“Translation is a gift of the Spirit, and people who come here need to have access to that gift. We try to do everything we can to help them prepare for that."

I like to think we’re saying much the same thing in our different ways.


REFERENCES
Christine Rappleye. Translators and interpreters needed for more than Conference. Mormon Times, April 7, 2010. http://mormontimes.com/around_church/worldwide_church/?id=14262.

Missionary (LDS Church). Wikipedia. http://en.wikipedia.org/wiki/Missionary_(LDS_Church).














Yuta Uemura interprets a church elder’s speech into Japanese during a meeting at the Church of the Latter-day Saints Conference Center in Salt Lake City.
Notice the screen for providing a close-up view of the speaker at a distance. Years ago, a colleague of mine used to bring a pair of binoculars into the booth for that purpose.

Photo by Kristin Murphy, Deseret News.

Machine and Human Translation 3: Pragmatics

I ended the previous post in this series (April 2) by saying that in the same way as MT systems ‘borrow’ COMAL, they can recycle another human translator competence.

Let me illustrate by an example, a very old one. Back in 1970, the MT project where I was working in Montreal received a visit from a distinguished French computer scientist, the late Bernard Vauquois of the University of Grenoble (see photo). Incidentally, he was co-founder of the International Committee on Computational Linguistics, which I mentioned in the preceding post. One of our team, Michel van Canaghem, went to the airport to drive him into town. We wanted to impress him, and to that end we set up a little demonstration of the system we were in the process of developing. It was called Q-Systems - Q for Quebec - and it had just been invented for us by another brilliant French computer scientist, Alain Colmerauer. We got together with Prof. Vauquois in our offices, where we had recently received our first terminal, a clunky Telex machine. From there we submitted an English sentence for translation into French on the university’s mainframe computer three storeys below. It could only be a single sentence, because we only had 128K of CPU (equivalent to RAM) at our disposal on the mainframe. Remember it was 1970! The sentence was this:

Prof Vauquois was met at the airport by Michel.

And the translation came back:

Michel a rencontré M Vauquois à l’aéroport.

We were proud. On the face of it a simple sentence, but the system had transformed the English passive construction to a French active one, which was better French, and it had changed the order of the constituents to put à l’aéroport at the end. We waited for Prof. Vauquois to congratulate us.

Not so. Instead he exclaimed, “Ah, mais ça ne va pas!” (Oh, but there’s something wrong!”). And he continued, “Michel didn’t meet me by chance. He was there waiting for me. The verb should be accueilli (received me, welcomed me), not rencontré.

Disappointed, we nevertheless saw what he meant. We might argue over the use of rencontré, but it’s undoubtedly ambiguous as to intent (just as met is), whereas acceuilli isn’t. But worse than that, we knew our system had no way to resolve such an ambiguity. It had no pragmatics. Pragmatics?

Pragmatics is a subfield of linguistics which studies the ways in which context contributes to meaning.

The ability to understand language according to context is something humans have and MT systems don’t, at least not unless they borrow it from human translations. The experience was a revelation to me; that’s why I remember it so clearly.

There’s a sequel to this story. Forty years later, we might expect that MT and the computers supporting it had advanced enough to make child’s play of translating such a simple sentence. So let’s find out. First I submitted it to Microsoft’s Bing Translator. Here’s the output:

Prof Vauquois a été rencontré à l'aéroport par Michel.

’Nuf said. We did as well or better in 1970. So let’s move on to the leader at the moment, Google Translate:

Prof Vauquois a été accueilli à l'aéroport par Michel.

Bravo! There’s accueilli. My guess is that Google Translate found it in human translations of other texts where meet is accompanied by at the airport or something very similar.

So far so good. And yet there’s still a disappointment. It concerns Prof Vauquois in the output. It’s grammatically incorrect. If we want to use this title, we have to write Le professeur Vauquois with the definite article. In 1970, we dealt with it in a different way, so we can’t compare on this point; but it’s very disappointing that a system which does well on simulated pragmatics falls down on a fairly elementary point of grammar. It’s all very well to find fragments of translation in the translation memory, but we would prefer them to be strung together into correct sentences and paragraphs, and it seems that’s still a problem for this method.

More to come.

REFERENCES
Christian Boitet et al. Bernard Vauquois, pioneer of machine
translation, Computational Linguistics, 12:1.43-47, 1986.

Alain Colmerauer. Les Systèmes-Q ou un formalisme pour analyser et synthétiser les phrases sur ordinateur (Q-Systems, a formalism for parsing and generating sentences by computer). Publication interne Nº 43. Université de Montréal, Département d'informatique, September 1970.

http://en.wikipedia.org/wiki/Pragmatics.

Bing Translator. http://www.microsofttranslator.com/Default.aspx.

Google Translate. http://translate.google.com.

Photo: Harcourt, Paris

Machine and Human Translation 2: Recycling COMAL

In my post about COMAL last week, I said that MT lacked the monitoring made possible by the human competence comparison of meaning across languages. I ought to have qualified it by saying that the sophisticated MT systems available today do possess a sort of recycled COMAL. They inherit it because they borrow segments (words, phrases, sentences) from previously existing translations. The latter are stored alongside their originals in large data banks called translation memories, and the systems recycle them by using statistical methods to match segments of new text with segments of old ones. But the translations in the translation memories have all been done by humans; so that in importing a translation from a memory, the MT is benefitting second hand from the COMAL of a human translator. This approach has proven to be a real breakthrough in MT, nevertheless it still has drawbacks. One of them is that the matching algorithms work on the outward form of the segments, not their meaning, and the same form may have more than one translation in different real-world contexts. Here’s an example from Google Translate:

Spanish input: En estos momentos, el presidente español ocupa la presidencia de la Union Europea.
English output: At present, the Spanish president holds the
presidency of the European Union.

Apparently very good. Grammatically perfect. And Google will even speak the translation for you! Except that my human COMAL instantly alerts me that something is amiss here: hey, Spanish president doesn't mean the same as presidente español. Spain doesn’t have a president! Its head of state is the king. Presidente refers to what in English is called the prime minister. And that brings us to the crux of the matter. Human COMAL works because we relate both the source text and the translated text to independent referents, in this case a political institution. The referents of both must coincide.

The team at Google led by principal linguist Franz Och (see photo) are well aware of the frustration their clientele sometimes feels. So they offer a way to vent the frustration constructively. They have added a link to Google Translate that invites users to “Contribute a better translation” for modifying the translation memory. Note that no qualifications are required in order to contribute. Expert, Native and even Natural Translators are all welcome. In this way the system becomes marginally interactive and more collective. It is already collective insofar as the contents of the translation memory represent the combined production of many human translators. No wonder this kind of MT system is called hybrid.

In the same way as they ‘borrow’ COMAL, MT systems can import another human translator competence. See the next post in this series.

REFERENCES
Peter F. Brown and colleagues at the IBM Thomas J Watson Research Center. A statistical approach to machine translation. Paper to the 12th International Conference on Computational Linguistics (COLING 88), Budapest, 1988. A later version was published in Computational Linguistics, 16:2.79-85, 1990. This was the seminal paper on mining translation memories for MT.

Miguel Helft. Google's computing power refines translation tool. The New York Times, New York edn., 9 March 2010, p. A1 =
www.nytimes.com/2010/03/09/technology/09translate.html.

Google Translate. http://translate.google.com.

There’s an article on Translation Memory in Wikepedia.

Photo: Peter DaSilva for The New York Times.