Якщо в минулому багато проектів з м

Якщо в минулому багато проектів з машинного перекладу базувалися на правилах то у Verbmobil було вирішено застосувати гібридний підхід. Згенерувавши можливий переклад з застосуванням багаторівневого лінгвістичного аналізу та переклад з застосуванням статистичних методів, обирається найкращий. За оцінками перекладачів, з 25000 перекладених прикладів 74,2% були перекладені правильно. Або, іншими словами, статистичні методи є простими у застосуванні, хоча переклад не завжди є влучним, а використання семантики вимагає багато часу, але продукує якісніший переклад. Але є учасники проекту, що стверджують — його “витягнули” саме статистичні методи. Тому особливо цікавим було б побачити детальні результати окремо по кожній з підсистем: тій, що використовувала лінгвістичний аналіз, і тій, котра використовувала статистичні методи. Цікаво, тому що вперше був виконаний глибинний аналіз для трьох мов — від розпізнавання слів та речень, і до семантики дискурсу, були використані сучасні формалізми, зокрема, в області синтаксису HPSG, дискурсу — теорія представлення дискурсу DRT. І, певна річ, важливим було б оцінити, наскільки здійснення й впровадження глибинного лінгвістичного аналізу покращують якість машинного перекладу.

Результаты (английский) 1:

If in the past a lot of projects with machine translation based on the rules in the Verbmobil was decided to use a hybrid approach. Generating the possible translation using multi-level linguistic analysis and translation of the application of statistical methods is best. According to translators, with 25,000 translated examples 74,2% were translated correctly. Or, in other words, statistical methods are simple to use, although the translation is not always on the mark, and the use of semantics requires a lot of time, but produces a better translation. But there are project participants that claim — it "dragged" is the statistical methods. Therefore, it would be particularly interesting to see the detailed results separately on each of the subsystems: the, which used the linguistic analysis, and one which used statistical methods. Interesting, because the first time was made a deep analysis for three languages — from the recognition of words and sentences, and the semantics of discourse have been used by modern formalisms of systems, in particular in the field of syntax HPSG, discourse representation theory discourse DRT. And, of course, it would be important to assess how implementation and introduction of deep linguistic analysis to improve the quality of machine translation.

Результаты (английский) 2:

Whereas in the past a lot of projects with machine translation based on rules Verbmobil then decided to use a hybrid approach. Generating possible translation using multi-linguistic analysis and translation using statistical methods, elected best. According translators, with 25,000 examples of translated 74.2% were translated correctly. Or, in other words, statistical methods are simple to use, although the translation is not always accurate, and the use of semantics requires a lot of time, but produces higher quality translation. But there are project participants that claim - it "pulled" is statistical methods. It is therefore particularly interesting to see detailed results separately for each of the subsystems to that used linguistic analysis, and the one that used statistical methods. I wonder why that was first performed in-depth analysis of three languages ​​- recognition of words and sentences, and the semantics of discourse used modern formalism, particularly in the area of ​​syntax HPSG, discourse - discourse representation theory DRT. And, of course, important to assess whether the implementation and application of deep linguistic analysis to improve the quality of machine translation.

Результаты (английский) 3:

If last many projects with machine translation based on the rules of the Verbmobil it was decided to apply hybrid approach.Generating a possible translation with the use of multilevel linguistic analysis and translation of statistical methods is elected by the best. According to the estimates of the translators,With 25000 with examples of 74.2 percent were translated into correctly. Or in other words, statistical methods are simple to use, although the translation is not always accurate, and use semantics requires much time,But generates struc translation. But there are participants of the project, which according to its "pulled" this kind of statistical methods. So was especially interesting to see detailed separately for each subsystems: age groups,What used linguistic analysis, and age groups, who used the statistical methods. Interesting, because was first implemented in-depth analysis for three languages - from the face of words and sentences,And semantics discourse, were used modern формалізми, in particular, in the field of syntax, HPSG discourse - the theory of discourse DRT. Naturally, this was an important would have to evaluate,As far as the exercise and introduction of in-depth linguistic analysis of improving quality of machine translation.