Dear all, 20/2/09
At 18:30 +0000 20/02/09, FERENC KOVACS wrote:
To see a sample, see the actual footage below:
<http://www.firkasz.com/news.php>http://www.firkasz.com/news.php
interesting...
Ferenc Kovacs
alias Frank
Genezistan
"Starting all over"
+44 7770654068 (Vodafone)
<http://www.firkasz.com/>www.firkasz.com and
<http://translationjournal.net/journal/46meaning.htm>http://translationjournal.net/journal/46meaning.htm <http://www.facebook.com/album.php?aid=2003546&l=1e704&id=1107563373>http://www.facebook.com/album.php?aid=2003546&l=1e704&id=1107563373
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COMMENTS
Interpreters are known to refuse to be taped,
because what they produce, once transcribed,
would be judged by Kovács úr "not a serious text,
but a farce or a parody".
He would be right, of course. But interpreters
are certainly paid more by the hour that serious
translators of written texts...
Why?
Simply, the task is not the same.
And interpreters do a very good job of conveying
most of the meaning of a monologue or a
discussion, under real-time constraints and
lexical stress.
Cognitively speaking, interpreting is much more tiring than translating.
Now, the situation is the same with MT of text
and speech, relative to translators and
interpreters.
Case 1: MT is made and used for helping
translators like Kovács úr. For example, with
what Morphologics offers now, between Hungarian
and 32 other languages, notably English, Russian,
etc., he could probably increase his productivity
by 2 to 3 -- but only if he postedits (reading
always the source segment before looking at the
"pretranslation" and trying to make a good
translation out of it) instead of trying to
revise (reading the MT result first). I
personnally recently postedited (online) results
of Systran EF on rather technical texts on water
and ecoloy at a rate of 500-800 words/hour, on
7000 segments.
Case 2: MT is there to help people understand
written or spoken utterances, and there is no
translator and no interpreter there to do the job.
- obviously, there can't be one when you browse
web pages, and no translator could possibly
translate (even "pretranslate" a web page in 1
second, which is less than the time to read it in
the first place). Again, this is another task.
- for speech, there is also a practical and
financial impossibility: no TV channel coulde
hire interpreters round the clock to interpret
into 22 other European languages.
These tasks are again different from the "help"
tasks, and from the "human" tasks.
Now, looking at the footage kindly shown by
Kovács úr, with no sound, the only thing I can
say is that the efficiency of this system
(containing no MT) is quite high, as I can
understand not only the general topic of the
discussions, but also most of the utterances,
despite the numerous errors. Many of these errors
would admittedly no be done by humans, but if a
stenotypist would transcribe and her output would
be fed into a program to turn it into correct
running text (IBM-France did it in the 80's, it
may be commercial), the stenotypist would stop
working after some time and then we would have
nothing.
Case 3: MT is there to help normal people (I want
to say: not translators, not even real
bilinguals) translate in their domains from a
language they know only a little or not at all.
The "operational" architecture of the MT system
has to be different, because it is again another
task.
What can be done is to present the user with
- the source text enhanced with annotations in
his language (multiple "pidgin translation", a
term introduced in 1971 by Brian Harris,
professor of translatology, at that time director
of the TAUM project at UdM, Montréal),
- many candidate pretranslations, factorized in
such a way that 1 only appears, the "best
trajectory" in the underlying controlled
confusion network, and that it can be changed to
another one, or directly edited when the user has
understood the source segment, relying on those
"linguistic crutches" and his/her good domain
knowledge.
That is clearly another task, for different persons.
Case 4: MT is there to help speakers of different
languages converse (chat or spoken dialogue).
Here, there is a possibility that
- interlocutors know to some extent a common language (scenario of VerbMobil-1)
- they can use some level of interactive disambiguation
For example, Converser for HealthCare (by
M.Seligman, SpokenTranslation Inc) is designed
for helping health personnel in the US converse
with hispanophone patients and their families
about almost any topic, not just health and
medicine. To raise the quality level, the system
offers
. in-built controls (over the result of speech
recognition, and indirectly over translations,
using reverse translation)
. interactive word sense disambiguation in the source language.
Again, the task is different, as it depends on
the "translational situation", and the
"operational architecture" of the MT system has
to be different.
Conclusions:
1) one cannot judge MT in its various forms
"intrinsically", as if its task would be the same
as that of professional translators like Kovács
úr.
2) the particular system (speech-to-text) he
refers to seems to me to merit something like a
B+ (15/20) using a task-related measure.
3) the remark "You do not need 99 percent of the
functionalities available. Just think about
that." applies very well not only to MicroSoft
software, but also to speech recognition,
translation, etc. In this case (TV), we do not
need the 99% of text quality a professional
stenotypist followed by a program could produce
after a few seconds. What we need, and get, is
the 10% (2/20) of "linguistic quality", the
real-time behavior, and the ergonomy, that
together allow us to follow the TV show in real
time.
4) returning to MT: always remember the
evaluation of Systran Rus-Eng at Euratom (Ispra)
in 1972: it got 18/20 (A+) from its users
(nuclear scientistsà, and 2/20 (D--) by teachers
of translation.
All translators, please realise that MT has never
been there to replace you, but can help you a lot
more than translation memories in many cases.
Best regards,
Xan
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