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Machine Translation

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Machine Translation

After more than half a century, machine translation has evolved from science fiction to reality. Developers of machine translation systems no longer fret over being confronted by a problem without a solution, while translators no longer worry lest the "rise of the machines" should deprive them of their livelihood. While a computer can’t replace a human translator in all areas, machine translation is already being put to effective use, if only to accomplish a strictly limited set of tasks. It is important to know the operating principles of machine translation and its limitations and fields of application in order to understand when machine translation should and should not be used to avoid disappointment.

Kinds of Machine Translation Systems

Machine translation systems belong to one of the three categories: Rule-Based Machine Translation (RBMT) systems, Statistical Machine Translation (SMT) systems, and the most promising "hybrid systems" combining the benefits of RBMT and SMT. RBMT systems analyze the text and build the translation using built-in dictionaries and a set of rules applicable to the particular language pair. SMT systems rely on statistical analysis: large volumes of source text (amounting to millions of words) along with target translations performed by humans are loaded into the application. The application analyzes the statistics of interlingual matches, word usage, and syntactic structures, and relies on this analysis when choosing fitting translations. This process is known as self-training. The system can be also trained by human translators who edit the translations produced. The widely known Google Translate service uses this very principle. Owing to the self-training ability of statistical and hybrid machine translation systems, the translation quality improves as they accumulate linguistic data with each completed translation.

Dos and Don'ts of Machine Translation

The key merit of machine translation is the capacity to handle very large volumes of text quickly, which sometimes makes it more cost-effective than human translation. That said, one should always remember that the quality of machine translation will always be inferior to human translation, and should therefore be used only in particular cases.

First, machine translation is suitable for materials intended for internal use. For example, to get the gist of a foreign-language website, article, or letter, or find news reports on a particular event in international publications. Second, machine translation can be used on technical and highly-specialized texts that then undergo post-editing by experts in the field. In this case, the target text is used as an interlinear translation based on which the technical expert produces the final text, relying on knowledge of the subject area.

Many types of materials are not suitable for machine translation. Machines cannot be trusted with texts where inaccurate translation could jeopardize human health, a sophisticated device, or a major contract. In this case, the time saved does not justify the risks. Any documents associated with legal liability (contracts, warranty) must be processed by humans. Machine translation is not suitable for marketing materials where the text is actually recast in a new cultural context and recreated from scratch.

Generally adequate quality can be expected when translating strictly formalized technical texts, whereas literary translation and promotional texts are beyond the scope of machine translation.

Preliminary Text Preparation and Post-editing

Preliminary preparation of a text can considerably simplify the job of both the machine translation systems and editors tasked with refining the raw machine translation. Such preparation begins at the stage of composing source text. To this end, standards are developed for technical writers and authors, who abide by them to make the text easier to understand and translate, for machines and humans alike.

Following these three rules goes a long way in enhancing the quality of machine translation from English:

  1. Use of infinitive verb forms instead of gerunds
  2. Use of the active voice instead of passive voice
  3. Avoidance of compound sentences and homogeneous parts of sentences

Ideally, each sentence should convey a single logical thought. This rule in particular, which applies to all languages in equal measure, is the most effective of all.

It has been proven through practice that following these simple rules and adapting the machine translation system correctly can considerably simplify the post-editing of the target text. This gives an idea of the benefits that come with the formalization and standardization of texts prior to machine translation, be that management of text authoring with the use of special applications, pre-editing of text, or simply requiring the author to follow a basic set of the most effective rules.

Post-editing involves the refinement of a raw machine translation by an editor, who normally has special training and is experienced in handling machine-translated texts. In most cases, machine translation requires post-editing. However, it can occasionally be omitted, especially when texts are translated for internal use, as a means to get the overall gist of the text or to locate and select specific materials. The amount of time and labor involved in post-editing is one of the key factors to be considered when evaluating the cost effectiveness of machine translation. Literary, promotional and other texts that are not suitable for machine translation by definition are not subject to post-editing either: to improve text quality to a level achieved by human translation, the editors would have to rewrite the text from scratch, thus negating any benefits of machine translation.

Volume and Economy

Before resorting to machine translation, one should not only have a clear idea of the desired end result and realize the limitations of this method, but also bear in mind an additional factor. Machine translation systems require complex customization and improvement, including "training" in a particular subject area, without which they inevitably fall short of expectations. It is therefore logical to use machine translation only on large volumes of similar texts. Only in this case will it be cost effective to spend a certain amount of time on system training before applying machine translation and obtaining text that is suitable for post-editing. Meanwhile, when a job involves several dozen pages, attempting machine translation would be futile and more costly in the long run.

In conclusion, machine translation with post-editing can be worthwhile when dealing with large volumes of similar texts. Large translation volumes often pass through translation agencies that specialize in particular subject areas, so deploying fairly effective yet expensive machine translation solutions of the latest generation is economically justified in these cases. However, neither content providers (however large) nor freelance translators can make effective use of machine translation on their own.

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