Friesland op Mastodon

Het Twitter-alternatief Mastodon heeft enkele nadelen en enkele voordelen.

Een van de nadelen is dat het op Mastodon wat moeilijker is om mensen met dezelfde belangstelling te vinden dan op Twitter, omdat Twitter tweets in je feed zet op basis van je eigen activiteit. Je moet dus wat meer zoeken, maar dat kan met het zoekveld waarin je tags en adressen van Mastodon-leden kunt invullen. Daardoor heb je op Mastodon ook wel minder last van ongewenste berichten in je feed.

Een van de voordelen is dat Mastodon 500 karakters per bericht toelaat, in plaats van slechts 280. Dat lijkt geen groot verschil uit te maken: 500 tekens is nog altijd geen 500 woorden. Maar toch valt op dat berichten invoeren op Mastodon een stuk comfortabeler is dan op Twitter, en het verschijnsel van de “feuilleton-berichten” komt op Mastodon nauwelijks voor.

Een ander voordeel is dat Mastodon een gedentraliseerd systeem is. Dat was ook de oorspronkelijke bedoeling van internet: een gedecentraliseerd computernetwerk, zodat ondanks het uitvallen van één server de andere servers elkaar nog altijd kunnen bereiken. Twitter is echter sterk gecentraliseerd, Mastodon niet. Mastodon is zelfs zo sterk gedecentraliseerd dat iedereen die dat wenst op eigen initiatief een Mastodon-server kan opzetten. De software is immers vrij beschikbaar.

Dat hebben in Friesland enkele mensen gebruikt om een server voor Friesland te installeren. Het internetadres is https://mastodon.frl

Volgens hun eigen informatie is @mastodon.frl “voor iedereen die een warme band heeft met Friesland. De hoofdtaal is Nederlands, ook het gebruik van de Friese taal en andere regionale dialecten wordt gewaardeerd. Dus: van Friezen, voor Friezen, in de breedste zin van het woord!”

Taalmodel: verantwoording afleggen over iets

FOUT Het jaarverslag van 2019 is een verantwoording op de doelen die Jan zich in het jaarplan voor 2019 heeft gesteld.

GOED Het jaarverslag van 2019 is een verantwoording over de doelen die Jan zich in het jaarplan voor 2019 heeft gesteld.

VERKLARING

Een heftig geval.

Er zijn twee uitdrukkingen mogelijk:

– verantwoordelijk zijn voor iets

– verantwoording afleggen over iets

Aangezien hier het woord “verantwoording” wordt gebruikt, moet dus worden gecombineerd met “over”, zeker niet met “op”.

Artificial Intelligence and translation

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There are two things which shouldn’t be forgotten about AI in general:
First it relies heavily on human input. E.g. the Tesla AI Autopilot is based on a massive amount of pictures on which objects are lined out, so that the software can compare those line-outs with images captured by the LIDAR (which is just some kind of visible light camera system). Without all those people analyzing the photographs, that Autopilot would not function. In other words: without the back-up of human intelligence, that artificial intelligence wouldn’t work.
Secondly: the input is analyzed by statistical means, not be actually intelligent systems in the way we understand ‘intelligence’. That means AI prefers frequent occurences of phenomena, not necessarily correct occurences of phenomena, i.e. not necessarily occurences which are the results of laws of nature.
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Vertaaltips: Terms and conditions

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“Terms and conditions” is een van die termen in de Engelse juridische en semi-juridische literatuur die voor beginnende vertalers een probleem vormt.

Zelfs goede woordenboeken laten je min of meer in de steek, want zowel “conditions” als “terms” worden wel eens als “voorwaarden” vertaald, en “voorwaarden en voorwaarden” is geen goede vertaling. Maar “Terms and conditions” wordt ook als “voorwaarden” vertaald.

Hoe pak je dat aan?

De meest handige oplossing is om “Terms and conditions” te vertalen als “Algemene voorwaarden”.

“Conditions” apart zijn dan “Voorwaarden”, en “terms” worden “bepalingen”.

Als de termen apart worden gebruikt is het echter altijd aan te raden om goed op de context te letten, want “terms” zijn soms “betalingsvoorwaarden” of “afbetalingsvoorwaarden”. De betekenis hangt af van de tekst in haar geheel.

Maar er kunnen ook bepalingen worden gebruikt, zoals “Terms of use”. En dat betekent “Gebruiksvoorwaarden”, niet “Bepalingen van het gebruik”.

En daarom zijn automatische vertalingen zo’n sof.

Nederlandse Taalunie organiseert enquête over kennis Nederlands van vertalers

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Enquête taalvaardigheid Nederlands van vertalers

Vertaaldiensten merken dat de taal van de nieuwe generatie vertalers verschilt van die van vorige generaties; woordenschat, stijl en register die jonge vertalers gebruiken, sluiten niet altijd aan op de veeleisende beroepspraktijk. Daarom wil de Taalunie samen met de vertaalsector en het vertaalonderwijs nadenken over de taalvaardigheid Nederlands van vertalers, en over de aansluiting tussen opleiding en beroepspraktijk. Als eerste stap willen wij u graag vragen om deze enquête in te vullen. Dit duurt ongeveer 10 minuten.

Ga naar de enquête

De resultaten van de enquête zullen we op de website van de Taalunie publiceren. Ook worden de resultaten gebruikt ter voorbereiding van een expertbijeenkomst rond dit onderwerp die de Taalunie samen met ITV Hogeschool voor Tolken en Vertalen, de Universiteit Utrecht en het Platform Taalbeleid in het Hoger Onderwijs organiseert op 27 juni 2019.

Als u vragen of opmerkingen hebt, kunt u contact opnemen met Folkert de Vriend, Fedde van Santen of Gys-Walt van Egdom

Iate geeft u extra mogelijkheden

Message on Iate – Bericht op Iate – Message d’Iate

Please note that the possibility to search by URL in the new IATE is now available, together with a permanent link to the full entry view by friendly ID. See details under ‘IATE for developers’ in the footer.

Het is nu mogelijk om met de nieuwe IATE te zoeken met een URL naar Iate, en om een permanente link te gebruiken naar de volledige invoerweergave met een ID. Details onder “Iate for developers” in de balk onderaan.

Today on IATE

Today on the site Iate: InterActive Terminology for Europe, there is a banner at the bottom which reads:

The IATE partners are glad to announce the opening of the new version of IATE. The current version will be replaced with a fully revamped version the week of 12 November. We take the opportunity to thank you for your continuous use of IATE and look forward to better serving your needs with the new version.

Iate is a renowned European website for the translation of terminology in various fields to all official European languages.
Although it’s database is not complete, it is often used to provide official translations which are valid for the whole of the European Union.

 

Switch to https://iate.europa.eu/home for more information.

Will AI make translation an obsolete craft?

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Sometimes people use Google Translate to understand a website. And some people think something exists like computer programs which spew out translations without any hassle. It makes look translators as old fashioned craftsman who at best have a workshop in a tourist center or during an arts & crafts exhibition.

 

Does that image suit reality?

 

As Artificial Intelligence is on the rise, some people proclaim the death of the translator in ten or maybe even five years time.

 

But as a matter of fact, Artificial Intelligence is not something which will pop up all at a sudden. It has been influencing daily practices since the nineties, maybe even earlier.

Research into artificial translation or machine translation started as early as 1949, but as is often the case with IT, the name promises more than it delivers. Early applications did nothing more than automatically looking up words in an automated dictionary.

 

Some historians claim that the idea of machine translation may be traced back to the 17th century, when in 1629 René Decartes proposed a universal language, which would share one symbol in different tongues for equivalent ideas. But the actual field of “machine translation” appeared only for the first time in ‘Memorandum on Translation’ by Warren Weaver in 1949. Research started in 1951 at MIT by Yehosha Bar-Hillel. And in 1954 there was a surprising demonstation at Georgetown University when the Machine Translation research team showed off its Georgetown-IBM experiment system in 1954. As computers’ power increased, so did the results of artificial translation. But real progress was rather slow, and after the ALPAC Report of 1966 found that the ten-year-long research had failed to fulfill expectations, funding was greatly reduced. However, in 1972 a report by the Director of Defense Research and Engineering (DDR&E) reestablished the feasibility of large-scale MT because of the success of the Logos MT system in translating military manuals into Vietnamese during that conflict. And so again, war made progress (that is ironic).

 

So, considering the early starting date of the research at about 1949, probably induced by the advent of computers during the Second World War, progress was actually very slow. The problem is whether the computer program can actually understand human language, and whether that understanding is necessary to be able to translate.

 

Some would argue “yes”, and they try to find the rules which govern human language. Interesting in that respect was transformational-generative grammar or TGG. It’s philosophy is that human beings have a set of rules in their heads which forms meaning into meaningful sentences. So an English speaker would have a rule which puts the verb immediately following the subject, whereas a Japanese speaker would have a rule putting the verb at the end of the sentence.

 

Fact is, however, that you still have to be able to make the computer program to be able to grasp the meaning of what it has to say. But it is not the computer translation program building up the message to be translated. The message is already given in the source text.

To a certain degree, that simplifies matters: the program only has to be able to transform a message from a source text into a target text, in which source and target contain the same content, but encoded in different ways.

 

That’s, of course, an idea which appeals to programmers. You take a source, use TGG to derive it’s inner structure or deep structure, and use TGG of another language to build up a new surface structure. As simple as that.

 

It seems to be the most intelligent way to deal with artificial translation, but linguistics themselves are not always sure about the rules which one should put into TGG. And, anyway, TGG is meant to go from deep structure to surface structure, not the other way around. So, that leaves us with the problem of the analysis of the source text. All TGG rules have to be “reversed” or “inversed”.

 

Although there are a lot of other ways to deal with automatic translation, not all of them could be implied from the very beginning. The advantage of a TGG based translation system was the promise of using rules in a way a human being processes language – or is thought to process language – thereby limiting the amount of memory. Rules, as in maths, provide a way to apply knowledge without a big knowledgebase. Compare having to learn al multiplications starting with the table of 1 till the table of 10, or only having to know the rule that you add up a number as many times as you want to multiply it.

 

Most machine translation systems try to apply rules, but not all do to the same degree. As a matter of fact, the terms ‘machine translation’, ‘automatic translation’, ‘artificial translation’ and so on, are not interchangeable.

 

The main rule-based machine translation (RBMT) paradigms are further classified in three types: transfer-based machine translation, interlingual machine translation and dictionary-based machine translation paradigms.

 

RBMT involves more information about the linguistics of the source and target languages. The basic approach uses a parser for the structure of the source sentence and an analyzer for the source language, and then applies a generator on that information to generate the target sentence, with a transfer lexicon for the translation of the words.

 

However, RBMT demands that everything is be made explicit: orthographical variation and erroneous input must be made part of the source language analyser in order to cope with it, and lexical selection rules must be written for all instances of ambiguity. Adapting to new domains in itself is not that hard, as the core grammar is the same across domains, and the domain-specific adjustment is limited to lexical selection adjustment. But, of course, that’s all from a theoretical point of view.

 

Another way is transfer-based machine translation. It creates a translation from an intermediate representation that simulates the meaning of the original sentence. Unlike interlingual MT, it depends partially on the language pair involved in the translation.

The third method, interlingual machine translation, is a kind of rule-based machine-translation. The source language is transformed into an interlingual language. That is a ‘language neutral’ representation that is independent of any language. The target language is then generated out of the interlingua. One of the major advantages of this system is that the interlingua becomes more valuable as the number of target languages it can be turned into increases. However, the only interlingual machine translation system that has been made operational at the commercial level is the KANT system (Nyberg and Mitamura, 1992), which is designed to translate Caterpillar Technical English (CTE) into other languages.

Using Caterpillar texts had the advantage of having an enormous load of already translated texts, and the fact that CTE is rather limited in scope: it only has to deal with technical language for heavy mobile equipment. Using it to translate other subject matters, would be disastrous.

 

The dictionary-based system uses a method based on dictionary entries, which means that the words will be translated as they are by a dictionary. This will make clear, of course, that a pure dictionary-based system can only give word-for-word translations, and therefore rather mediocre results – to put it mildly.

 

The statistical machine translation (SMT) uses bilingual text corpora. Where such corpora are available, good results can be achieved translating similar texts, but such corpora are still rare for many language pairs. Google switched to a statistical translation method in October 2007. In 2005, Google improved its internal translation capabilities by using approximately 200 billion words from United Nations materials to train their system, and the translation accuracy improved. Google Translate and similar statistical translation programs work by detecting patterns in hundreds of millions of documents that have previously been translated by humans and making intelligent guesses based on the findings. Generally, the more human-translated documents available in a given language, the more likely it is that the translation will be of good quality. However, it turned out this is not always the case, rather to the surprise of Google. Newer approaches into Statistical Machine translation use minimal corpus size and instead focus on derivation of syntactic structure through pattern recognition, which puts higher stress on artificial intelligence. SMT’s biggest downfall includes it being dependent upon huge amounts of parallel texts, its problems with morphology-rich languages (especially with translating into such languages), and its inability to correct singleton errors. Which explains why Google was disappointed. Not to mention that a typical United Nations document deals with a limited set of subjects.

 

Example-based machine translation is based on the idea of analogy. The corpus also contains texts that have already been translated. Given a sentence that is to be translated, sentences from this corpus are selected that contain similar sub-sentential components. The similar sentences are then used to translate the sub-sentential components of the original sentence into the target language, and these phrases are put together to form a complete translation.

Hybrid machine translation (HMT) leverages the strengths of statistical and rule-based translation methodologies. Several MT organizations claim a hybrid approach that uses both rules and statistics.

 

And finally a deep learning based approach is neural machine translation.

But all these methods are in some or other way hampered by several problems: ambiguity in texts, non-standard speech, names from people, places, organizations and so on, and the continuous changes in language: what’s standard today, might be substandard tomorrow, and vice-versa.

 

In reality all systems are in some way hybrid systems, because the output of the computer program always has to be checked by a human translator. Example-based machine translation is actually the most successful form of machine translation, because the computer program uses a big memory of previous translations to come up with suggestions, which the translator has to judge, change if necessary, and validate.

 

As mentioned above, forms of machine translations have a long history, and the development was slow and hampered by characteristics of human language (e.g. it’s well-know lack of sustained logic), and by technological problems, like processing speed and memory size.

The main reason computer translations seem to be on the up, is that processing speed and memory size are gradually less of a problem. It also means that the influx of all forms of automation have never given a big boom to artificial translation.

 

It did, however, change the nature of the work of the translator. Translation turned more and more into proofreading and editing, away from pure translation. That was a rather slow evolution, and in all likelihood, it will remain so for a very long time.

 

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