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The global market requires content that everyone can understand, so a properly localized website is a must. The COVID-19 pandemic has reminded us that content-rich and easy-to-navigate online platforms are the cornerstones of brand building. If you want to increase your slice of the ‘global pie’ refine your website’s content through culture, language, and flow. Never make the mistake of assuming that an English website will be accessible to most people.
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Even though the percentage of English-speaking users is still significant, the ratio of those who prefer other languages is catching up pretty fast. Interestingly, Russia takes the second spot in terms of traffic ranking. In the upcoming years, other languages will leave English behind. If you want to learn how NLP technologies help you in localizing your business, please continue reading.
Applying Neural Machine Translation to Localization
The localization of a business website involves translating and adapting the content to specific local markets. This involves language technologies. Needless to say, website localization can be an intimidating proposition. It’s not as simple as a straight translation. Thanks to natural language processing (NLP) technologies, you can grow your business right away.
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You’ll have a website that communicates effectively with the locale. Powered by machine learning and deep learning algorithms, these systems understand language in the same way a person does. Automatic translation capabilities have improved over the years. Not only is translation accurate, but also it captures the tone and sentiment of the input language.
What NLP does is take raw and written texts and interpret them. Cutting-edge NLP systems are capable of quickly analyzing chunks of texts to generate insights. Therefore, instead of manually translating a website into another language, you can resort to an NLP-powered translation tool to automatically translate the web pages.
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As more words get added to the MT engine, the more accurate it becomes. Machine translation depends on the number of words and sentences you give it. Website localization can be done in several ways, but there’s only one right way to get things done – using NLP. Natural language processing techniques help accelerate processes that only humans could do previously.
NLP Capabilities in Translation Might Sound Great, But Issues Can Arise
The world’s most successful companies speak their customers’ languages. Although NLP technologies are invaluable for website localization, they can’t be successfully used without some involvement from human experts. There are still a number of limitations, such as:
- Contextual words and phrases
- Domain-specific language
- Low-resource languages (Swahili, Vietnamese, Bengali, etc.)
- Irony and sarcasm
- Colloquialisms and slang
- Ambiguity
The good news is that it’s possible to compensate for NLP’s lack of understanding. Users often provide invaluable feedback that reveals overlooked design details by model developers. As a reminder, artificial intelligence can’t learn by itself. It relies on intensive human feedback. The localization process can be improved by introducing humans into the loop to tune the model. It’s the human-AI partnership that enhances the NLP system.
Human-In-the-Loop: Accelerating the AI Lifecycle
Human-in-the-loop learning (HITL) is a branch of artificial intelligence that brings together both human and machine intelligence to create machine learning models. The computer system can incorporate selected human inputs into the learning process. The outcome is a loop in which the machine constantly learns how to improve its capabilities.
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Machine translation driven by humans in the loop is an interesting and innovative approach. Until now, it hasn’t been applied to low-resource languages. As long as machine translation is here to stay, there’s a need for human translators to make sure everything is accurate and culturally attuned.
When the machine isn’t able to solve the problem, humans step in and intervene. With a relatively small set of feedback, human-in-the-loop learning can dramatically improve the model accuracy. It’s possible to collect both implicit and explicit feedback to reinforce learning.
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If you need to translate specific texts, you can benefit from automatically generated machine translations by taking part in a human-in-the-loop process. Using the HITL NLP tool yields positive results. You can translate from English to French, German, Japanese, and so on with surprising accuracy. In some cases, an NLP technology with human-in-the-loop can outperform traditional machine translation systems.
If Possible, Train the MT Engine Yourself, Using the Words and Phrases Your Company Uses the Most
To establish a customer base around the world, a product or service must support a wide array of locales. To address the challenges of business website localization, you can use natural language processing to automate the process. You can generate high-quality translations in many different languages and make them available in real-time across various platforms.
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The website shouldn’t be just for those who know English. Users and visitors prefer using their mother tongue, even if they speak a second or a third language. Some try to avoid localization at all costs, thinking that it’s time and resource-consuming, not to mention blatantly ineffective. This is far from the truth.
A machine translation engine requires volumes of quality training data. You must undertake training, tuning, and testing tasks and feed information back into the system so that it becomes smarter. The continuous feedback loop is especially effective during the translation process. It’s necessary to provide various types of feedback at different stages of the workflow to improve performance and usability.
The NLP system will collect the feedback and learn from it. Language is dynamic, and nothing can stop it from changing. It’s continuously changing, evolving, and adapting to the needs of its users. Consequently, there’s no substitute for the human touch when it comes down to ensuring localized content is presented in a suitable manner.
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It’s up to you to correct any inaccurate results that the machine produces. Instances that require your attention are when the algorithm isn’t confident about a judgment and when it’s overly confident about the wrong result. Show the NLP system how to make better decisions so that next time, it gets it right. If the rules are too complicated for automation or the ML algorithm doesn’t offer accurate results, it’s time to resort to humans.
