In today’s globally connected world, language barriers often hinder finding the information we need online. It is especially true when searching on a website in a language that is not our native tongue.

Traditional site search has a limited ability to overcome these language barriers. Yet, that is now changing with the advent of machine translation.

In this article, we will explore how machine translation breaks down language barriers in site search and improves the overall user experience.

The internet has made it possible for people worldwide to access information, but language barriers can still prevent many from finding what they need [1].

Even when using a website’s built-in translation tools, the quality of the translation is often poor, making it difficult to understand the information being presented.

It can lead to frustration and a lack of confidence in the search results, resulting in a poor user experience [2].

That said, machine translation comes in handy, especially for revenue-driving sites like e-shops.

Machine translation is a technology that uses artificial intelligence (AI) and machine learning algorithms to automatically translate text from one language to another. In site search, this technology is used to translate the search bar and results into the user’s preferred language [3].

This process happens in real time, so it is seamless and unobtrusive, making it easier for people to search for and find information on a website [4].

That is especially relevant for e-commerce stores that seek to provide user-relevant search experience, regardless of user language.

That’s why user experience-centered e-commerce search like LupaSearch automatically supports machine translations. How does it work?

With LupaSearch, e-shop visitors can type their search query in any language (let’s say, the Polish language).

Then, the machine translation (together with Natural Language Processing) translates the query in the background and delivers query-matching product suggestions for a user.

This way, the user receives relevant suggestions despite differences in languages.

Machine translation technology in site search has several benefits that can greatly improve the user experience:

Breaking down language barriers:

Firstly, machine translation allows for increased accessibility to information by breaking down language barriers [5].

It means that users are no longer limited by their knowledge of a particular language and can find information in their native language, making it easier to understand and trust the results.

As a result, this can lead to greater engagement with the website, as users are more likely to stick around and continue their search [6].

Improved user experience:

Another benefit of machine translation in site search is improved user experience. With the ability to search for and find relevant products in their preferred language, users can interact with the website more easily and effectively [7].

It can result in a more positive overall experience that can increase the likelihood of purchases.

Greater audience reach:

Expanded audience reach is also a key benefit of machine translation on site search. By being able to provide information in multiple languages, websites can reach a wider audience, including those who may not have been able to access the information before [8].

It can result in increased traffic beneficial for both businesses and individuals.

Improve global communication:

Finally, machine translation can enhance global communication and collaboration by making it easier for people from different parts of the world to access and understand information [9].

It can lead to greater cross-cultural exchange and collaboration, helping to bring people closer together and promote a more interconnected world.

Machine translation has been successfully implemented in many industries, and there are many real-world examples of its impact on site search.

One such industry is ecommerce, where websites use machine translation to reach a global audience and provide product information in multiple languages.

It allows the websites to reach customers from all over the world, regardless of their language proficiency, and provide them with the information they need to make informed purchasing decisions [4].

Another example of machine translation in the site search is the news industry. News websites use it to present articles in different languages, allowing people from all over the world to stay informed. It can help to bridge cultural divides and provide a more diverse range of perspectives on current events [2].

The travel industry is another area where machine translation has made a big impact. Travel websites use it to make it easier for travelers to find information about destinations, hotels, and activities in their native language. It can help to reduce the stress and confusion often associated with traveling to foreign countries and make the experience more enjoyable for travelers [7].

These real-world examples demonstrate the successful implementation of machine translation in site search and its ability to greatly improve the user experience by making it easier for people to find what they’re looking for in their preferred language.

While machine translation has many benefits, there are also some challenges and limitations that need to be considered.

For example, the accuracy and reliability of machine translation can vary greatly, depending on the language and the context [10].

There is also a need for regular maintenance and updates of the machine translation algorithms to ensure they remain effective and accurate [11].

Finally, there is the potential for misuse and misinterpretation of machine translation, which could lead to misunderstandings and communication breakdowns [12].

Conclusion

In conclusion, machine translation is breaking down language barriers and improving the overall user experience in site search.

By making it easier for people to find information in their native language, machine translation is expanding the reach of websites and increasing global communication and collaboration.

While there are still challenges and limitations to be considered, the benefits of machine translation in site search cannot be ignored. We can expect to see continued innovation and growth in this area in the years to come.

Are you ready to improve your e-commerce search relevance? Then integrate LupaSearch - a smart e-commerce search that seamlessly does translating for you.

Request a free product demo, and let’s have a productive conversation.

References

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  2. Reber, U. (2019). Overcoming Language Barriers: Assessing the Potential of Machine Translation and Topic Modeling for the Comparative Analysis of Multilingual Text Corpora. Communication Methods and Measures, 13, 102-125.

  3. Yamashita, N., & Ishida, T. (2006). Effects of machine translation on collaborative work. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 515-524.

  4. Calefato, F., Lanubile, F., & Minervini, P. (2010). Can Real-Time Machine Translation Overcome Language Barriers in Distributed Requirements Engineering?. 2010 5th IEEE International Conference on Global Software Engineering, 257-264.

  5. Magdy, W., & Jones, G. (2011). An efficient method for using machine translation technologies in cross-language patent search. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 1925-1928.

  6. Andrabi, S. A. B., & Wahid, A. (2022). Machine Translation System Using Deep Learning for English to Urdu. Computational Intelligence and Neuroscience.

  7. Calefato, F., Lanubile, F., Romita, D., Prikladnicki, R., & Pinto, J. H. S. (2014). Mobile Speech Translation for Multilingual Requirements Meetings: A Preliminary Study. 2014 IEEE 9th International Conference on Global Software Engineering, 145-152.

  8. NLLB Team, Costa-jussà, M., Cross, J., Celebi, O., Elbayad, M., Heafield, K., Heffernan, K., et al. (2022). No Language Left Behind: Scaling Human-Centered Machine Translation. ArXiv, abs/2207.04672.

  9. Pituxcoosuvarn, M., & Ishida, T. (2018). Multilingual Communication via Best-Balanced Machine Translation. New Generation Computing, 36, 349-364.

  10. Rivera-Trigueros, I. (2021). Machine translation systems and quality assessment: a systematic review. Language Resources and Evaluation, 56, 593-619.

  11. Li, G., Liu, L., Zhu, C., Wang, R., Zhao, T., & Shi, S. (2021). Detecting Source Contextual Barriers for Understanding Neural Machine Translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 3158-3169.

  12. Wu, D., & He, D. (2012). Exploring the further integration of machine translation in English-Chinese cross language information access. Program, 46, 429-457.