Evolution Of Machine Translation

[Nicholas Mahendra]

Theoriginal goal behind MT was to build computers that would perform Rule-based translation independently. In other words, the machine would be taught full vocabulary & grammar of multiple languages, so it may translate autonomously.

In 1954, the IBM 701 successfully translated 49 sentences on the topic of chemistry from Russian into English. Around this time, MT saw a shift in catering from military to civilian interests. Globalization called for greater integration of the markets worldwide. A basic understanding of the languages spoken across the world became a necessity for any commercial organization.

While an improvement upon the RBMT, SMT only recognizes commands while translating. It fails to translate the significations that humans associate with the language being translated. It excels at translating scientific and technical writing but cannot interpret colloquial or artistic language. For Example, a lot of traditional Chinese medicine names are unable to be translated as they do not have parallel English terms. They are further tied to Chinese culture which the SMT has no knowledge of.

The last and most recent stage of MT is Neural MT. Neural MT consists of neural networks trained and optimized to perform translation services. It uses deep learning to analyze vast amount of translations already performed by human translators. NMT cannot account for whole sentences, understand context, and account for different variations and work with linguistic subtleties that could never be programmed into a statistical model. As a result, NMT is more fluent and natural in its translation. It mimics the workings of the human brain in its ability to learn and form neural pathways. The structure of the neural network makes the system more adaptive to handle complex models than a system based on rules and statistics. It can also learn from its mistakes and adjust accordingly to perform efficiently next time.

NMT has already proved to be miles better than SMT. Yet, there is a long way to go before human translators can be replaced. The post editing of MT will open up new growth opportunities for translation service providers. The future of MT will be a symbiotic relationship between human translators and machine translators.

However, the triumphant headlines hid one little detail. No one mentioned the translated examples were carefully selected and tested to exclude any ambiguity. For everyday use, that system was no better than a pocket phrasebook. Nevertheless, this sort of arms race launched: Canada, Germany, France, and especially Japan, all joined the race for machine translation.

The vain struggles to improve machine translation lasted for forty years. In 1966, the US ALPAC committee, in its famous report, called machine translation expensive, inaccurate, and unpromising. They instead recommended focusing on dictionary development, which eliminated US researchers from the race for almost a decade.

This is the most straightforward type of machine translation. It divides the text into words, translates them, slightly corrects the morphology, and harmonizes syntax to make the whole thing sound right, more or less. When the sun goes down, trained linguists write the rules for each word.

In contrast to direct translation, we prepare first by determining the grammatical structure of the sentence, as we were taught at school. Then we manipulate whole constructions, not words, afterwards. This helps to get quite decent conversion of the word order in translation. In theory.

In practice, it still resulted in verbatim translation and exhausted linguists. On the one hand, it brought simplified general grammar rules. But on the other, it became more complicated because of the increased number of word constructions in comparison with single words.

Even if anyone were to succeed in creating an ideal RBMT, and linguists enhanced it with all the spelling rules, there would always be some exceptions: all the irregular verbs in English, separable prefixes in German, suffixes in Russian, and situations when people just say it differently. Any attempt to take into account all the nuances would waste millions of man hours.

Japan was especially interested in fighting for machine translation. There was no Cold War, but there were reasons: very few people in the country knew English. It promised to be quite an issue at the upcoming globalization party. So the Japanese were extremely motivated to find a working method of machine translation.

Rule-based English-Japanese translation is extremely complicated. The language structure is completely different, and almost all words have to be rearranged and new ones added. In 1984, Makoto Nagao from Kyoto University came up with the idea of using ready-made phrases instead of repeated translation.

EBMT showed the light of day to scientists from all over the world: it turns out, you can just feed the machine with existing translations and not spend years forming rules and exceptions. Not a revolution yet, but clearly the first step towards it. The revolutionary invention of statistical translation would happen in just five years.

In early 1990, at the IBM Research Center, a machine translation system was first shown which knew nothing about rules and linguistics as a whole. It analyzed similar texts in two languages and tried to understand the patterns.

Model 2 dealt with that: it memorized the usual place the word takes at the output sentence and shuffled the words for the more natural sound at the intermediate step. Things got better, but they were still kind of crappy.

This method is based on all the word-based translation principles: statistics, reordering, and lexical hacks. Although, for the learning, it split the text not only into words but also phrases. These were the n-grams, to be precise, which were a contiguous sequence of n words in a row.

Besides improving accuracy, the phrase-based translation provided more options in choosing the bilingual texts for learning. For the word-based translation, the exact match of the sources was critical, which excluded any literary or free translation. The phrase-based translation had no problem learning from them. To improve the translation, researchers even started to parse the news websites in different languages for that purpose.

Starting in 2006, everyone began to use this approach. Google Translate, Yandex, Bing, and other high-profile online translators worked as phrase-based right up until 2016. Each of you can probably recall the moments when Google either translated the sentence flawlessly or resulted in complete nonsense, right? The nonsense came from phrase-based features.

The problem is, the syntactic parsing works terribly, despite the fact that we consider it solved a while ago (as we have the ready-made libraries for many languages). I tried to use syntactic trees for tasks a bit more complicated than to parse the subject and the predicate. And every single time I gave up and used another method.

The question is, what type of neural network should be used for encoding and decoding? Convolutional Neural Networks (CNN) fit perfectly for pictures since they operate with independent blocks of pixels.

In two years, neural networks surpassed everything that had appeared in the past 20 years of translation. Neural translation contains 50% fewer word order mistakes, 17% fewer lexical mistakes, and 19% fewer grammar mistakes. The neural networks even learned to harmonize gender and case in different languages. And no one taught them to do so.

The most noticeable improvements occurred in fields where direct translation was never used. Statistical machine translation methods always worked using English as the key source. Thus, if you translated from Russian to German, the machine first translated the text to English and then from English to German, which leads to a double loss.

In 2016, Google turned on neural translation for nine languages. They developed their system named Google Neural Machine Translation (GNMT). It consists of 8 encoder and 8 decoder layers of RNNs, as well as attention connections from the decoder network.

Yandex launched its neural translation system in 2017. Its main feature, as declared, was hybridity. Yandex combines neural and statistical approaches to translate the sentence, and then it choose the best one with its favorite CatBoost algorithm.

The thing is, neural translation often fails when translating short phrases, since it uses context to choose the right word. It would be hard if the word appeared very few times in a training data. In such cases, a simple statistical translation finds the right word quickly and simply.

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AbstractThe localisation sector is highly technologized and evolves rapidly. Though significant consideration has been given tothird-level training in localisation for Translation Studies students, the nature of the industry is such that this topic demands regularattention. Our objective was to survey employees and executive managers to understand what impact recent technological developments,including but not limited to neural machine translation (NMT), might have on future skills and training requirements for localisationlinguists. Our findings are that linguists in localisation take up a variety of roles, including transcreation, data mining, and project andvendor management. NMT is considered an important advancement, and its introduction has emphasised the need for a critical use oftechnology, while opening new career pathways, such as data curation and annotation. Domain specialisation is recommended for those whotranslate, and transferable soft skills are more essential than ever. Increased industry and interdisciplinary collaborations in trainingare also considered valuable.

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