Will Machine Translation Overpower Manual Translation In The Future?

Machine translation is the task that requires a text to be translated from its source language to a different language. It holds tremendous potential in multilingual countries like India. There are Translation Agencies in Delhi that can translate documents within hours for a quoted price.

Types And Origins Of Machine Translation

Machine translation as a task is quite old, originating in the 1970s. This field gained momentum in the 1980s in India. Worldwide, three major approaches to machine translation have emerged over the years:

• Neural Machine Translation (NMT)
• Statistical Machine Translation (SMT)
• Rule-based Machine Translation (RBMT)

Rule-Based Machine Translation

RBMT is the oldest machine translation system and works based on sourcing the dictionaries and grammars of the original text language and target language, including the languages' morphological, semantic, and syntactic regularities. It utilizes this retrieved information to generate sentences. RBMT systems were initially developed in the early 70s. RMBT systems further fall under three types:

• Interlingual RBMT systems.
• Direct Systems.
• Transfer Based RBMT systems.

Statistical Machine Translation

Statistical Machine Translation (SMT) is a machine translation method. It functions by deriving parameters from the analysis of the bilingual text corpora and then generating the translation. This method contrasts with the RMBT as a mentioned earlier method. Warren Weaver first proposed this idea in the year 1949. SMT was re-introduced in the late 80s by the researchers at IBM. Statistical Machine Translation was widely studied up until the introduction of the neural machine translation. Some benefits from SMT systems were:

• Efficient use of human and data resources
• Fluent translations thanks to the use of a language model.

However, SMT systems are not without fault, as may be noted below:

• Creating Corpus for SMT systems was expensive.
• SMT translation systems are less effective at translating language pairs that have very different word orders.
• Some errors and phrases were hard to predict and fix.

Neural Machine Translation

Neural Machine Translation (NMT) is a method of machine translation that relies on a neural network to predict and translate sentences. Neural Machine Translation systems are trained from end-to-end, which aids in maximizing translation performance.

Challenges Faced By Machine Translation

Machine translations have yet to overcome many challenges before they overtake human translators. It is hard for them to capture cultural nuances, such as jokes and idioms. These nuances are easily lost when using machine translation, ultimately requiring human intervention.

The other fact of the matter is that as languages keep evolving, new phrases and words are added to an ever-expanding vocabulary. While these changes can certainly update machines, human translators can adapt much quicker in these circumstances.

Words can also have multiple meanings, and machine translators can struggle to choose the right words. As mentioned earlier, post-editing in the form of human translators can help in maximizing the accuracy of the machine translators.

Conclusion

Machine learning has a long way to go before it can catch up with human translators. It does have merits, and there is plenty of potential in Neural Machine Translators. In the meantime, if you're looking to translate documents of your own, make sure you read testimonials and compare quotes and features to determine what would be the Best Translation Agency in Delhi or anywhere else in India.

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