Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Fast-learning computer translates from four languages

21.02.2008
Modern approaches to machine translation between languages require the use of a large ‘corpus’ of literature in each language. Now a European project has demonstrated a cheaper solution which compares favourably with the market leaders in translating from Dutch, German, Greek or Spanish into English.

The European Union now has 23 official languages. That means documents written in one language may need to be translated into any of 22 others, a total of 253 possible language pairs. Small wonder that the institutions of the European Union, and organisations dealing with international commerce, among others, have a keen interest in automating the process where they can.

Efforts to use computers to translate languages, known as machine translation, date from the 1950s, yet computers still cannot compete with human translators for the quality of the results. Machine translation works best for formal texts in specialised areas where vocabulary is unambiguous and sentence patterns are limited. Aircraft manufacturers, for example, have devised their own systems for quickly translating technical manuals into many languages.

The EU has been active in promoting research in this field since the large Eurotra project of the 1980s. In common with other projects of the time, Eurotra used a ‘rules-based’ approach where the computer is taught the rules of syntax and applies them to translate a text from one language to another. This is also the basis of most commercial translation software.

But since the early 1990s the new concept of ‘statistical’ translation has gained ground in the machine translation community, arising out of research into speech recognition. This dispenses with rules in favour of using statistical methods based on a text ‘corpus’.

A corpus is a large body of written material, amounting to tens of millions of words, intended to be representative of a language. Parallel corpora contain the same material in two or more languages and the computer compares the corpora to learn how words and expressions in one language correspond to those in another. An important example is a parallel corpus of 11 languages based on the proceedings of the European Parliament.

Pattern matching
“Parallel corpora are expensive and rare,” says Dr Stella Markantonatou, of the Institute for Language and Speech Processing in Athens, which coordinates the EU’s METIS II project. “They exist only for a very few languages and in small amounts and in specialised texts. So our idea was to try to do statistically based machine translation without this resource, using just monolingual corpora of the target language. For instance, to translate from Greek into English we use a large English corpus.”

To use a single corpus you need a dictionary for the vocabulary and a way to understand the syntax. In the original METIS project, completed in 2003, the corpus was processed to analysis sentence patterns and the text to be translated was then matched against the patterns.

In Greek, for example, the verb can precede the subject of a sentence. “So if you come in with a Greek sentence, ‘Eats Mary a cake’, you would like the machine to be able to translate it into English and rearrange the words to make ‘Mary eats a cake’,” explains Dr Markantonatou. “Pattern matching is a good way of doing that because it is able to take patterns from the source language and make them like the target language.”

METIS II takes the principle further by matching patterns at the ‘chunk’ level, a phrase or fragment of a sentence rather than a sentence as a whole, as this makes the pattern matching more efficient.

It can also use grammar rules to generate alternative possibilities for the translation and then use the corpus to identify which is the more probable. For example, where English would say ‘I like cakes’, some European languages might use the form ‘cakes please me.’ So in translating into English, METIS II can test alternative interpretations against the English language corpus. In this example, 'cakes please me' would get a very low score while the closest match 'I like cakes' would score highly.

Four languages
The partners have now built a system that translates from Greek, Spanish, German or Dutch into English. Trials so far show that it performs well in comparison with SYSTRAN, the rules-based market leader in machine translation. Considering that SYSTRAN is based on half a century of development while METIS II has only run for three years, that is quite an achievement. A prototype is already available on the internet.

The problem now is what to do next. Results from METIS II are being followed up in national research programmes in Spain and Belgium, but there are no plans as yet to further develop the whole system. Some of the components created in the project, such as dictionaries and associated language tools, could be marketable in their own right, but would need an industrial partner to provide the investment needed to turn the prototype into a commercial product.

“For Greek, it would be an excellent opportunity because there is nothing really good for [translating it] at present,” Dr Markantonatou tells ICT Results. “With a better lexicon, fixing bugs and making algorithms more efficient, this kind of thing could work. In another two or three years, METIS could be a very serious competitor to SYSTRAN. It’s a matter of funding.”

Christian Nielsen | alfa
Further information:
http://cordis.europa.eu/ictresults/index.cfm/section/news/tpl/article/BrowsingType/Features/ID/89533

More articles from Information Technology:

nachricht Stable magnetic bit of three atoms
21.09.2017 | Sonderforschungsbereich 668

nachricht Drones can almost see in the dark
20.09.2017 | Universität Zürich

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: The pyrenoid is a carbon-fixing liquid droplet

Plants and algae use the enzyme Rubisco to fix carbon dioxide, removing it from the atmosphere and converting it into biomass. Algae have figured out a way to increase the efficiency of carbon fixation. They gather most of their Rubisco into a ball-shaped microcompartment called the pyrenoid, which they flood with a high local concentration of carbon dioxide. A team of scientists at Princeton University, the Carnegie Institution for Science, Stanford University and the Max Plank Institute of Biochemistry have unravelled the mysteries of how the pyrenoid is assembled. These insights can help to engineer crops that remove more carbon dioxide from the atmosphere while producing more food.

A warming planet

Im Focus: Highly precise wiring in the Cerebral Cortex

Our brains house extremely complex neuronal circuits, whose detailed structures are still largely unknown. This is especially true for the so-called cerebral cortex of mammals, where among other things vision, thoughts or spatial orientation are being computed. Here the rules by which nerve cells are connected to each other are only partly understood. A team of scientists around Moritz Helmstaedter at the Frankfiurt Max Planck Institute for Brain Research and Helene Schmidt (Humboldt University in Berlin) have now discovered a surprisingly precise nerve cell connectivity pattern in the part of the cerebral cortex that is responsible for orienting the individual animal or human in space.

The researchers report online in Nature (Schmidt et al., 2017. Axonal synapse sorting in medial entorhinal cortex, DOI: 10.1038/nature24005) that synapses in...

Im Focus: Tiny lasers from a gallery of whispers

New technique promises tunable laser devices

Whispering gallery mode (WGM) resonators are used to make tiny micro-lasers, sensors, switches, routers and other devices. These tiny structures rely on a...

Im Focus: Ultrafast snapshots of relaxing electrons in solids

Using ultrafast flashes of laser and x-ray radiation, scientists at the Max Planck Institute of Quantum Optics (Garching, Germany) took snapshots of the briefest electron motion inside a solid material to date. The electron motion lasted only 750 billionths of the billionth of a second before it fainted, setting a new record of human capability to capture ultrafast processes inside solids!

When x-rays shine onto solid materials or large molecules, an electron is pushed away from its original place near the nucleus of the atom, leaving a hole...

Im Focus: Quantum Sensors Decipher Magnetic Ordering in a New Semiconducting Material

For the first time, physicists have successfully imaged spiral magnetic ordering in a multiferroic material. These materials are considered highly promising candidates for future data storage media. The researchers were able to prove their findings using unique quantum sensors that were developed at Basel University and that can analyze electromagnetic fields on the nanometer scale. The results – obtained by scientists from the University of Basel’s Department of Physics, the Swiss Nanoscience Institute, the University of Montpellier and several laboratories from University Paris-Saclay – were recently published in the journal Nature.

Multiferroics are materials that simultaneously react to electric and magnetic fields. These two properties are rarely found together, and their combined...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

Event News

“Lasers in Composites Symposium” in Aachen – from Science to Application

19.09.2017 | Event News

I-ESA 2018 – Call for Papers

12.09.2017 | Event News

EMBO at Basel Life, a new conference on current and emerging life science research

06.09.2017 | Event News

 
Latest News

Rainbow colors reveal cell history: Uncovering β-cell heterogeneity

22.09.2017 | Life Sciences

Penn first in world to treat patient with new radiation technology

22.09.2017 | Medical Engineering

Calculating quietness

22.09.2017 | Physics and Astronomy

VideoLinks
B2B-VideoLinks
More VideoLinks >>>