The internet has brought people from all over the world closer together, making it easy to connect with just a few clicks. But even with this global connection, language barriers can still get in the way of accessing information online.

That’s where Machine Translation in site search comes in.

It helps break down those language barriers and makes it easier for people worldwide to find what they’re looking for, no matter what language they speak.

What is Machine Translation?

Machine Translation uses AI to automatically translate text. When applied to site search, both the search queries and the results can be translated in real time.

This allows users to search in their preferred language and get results in that same language.

This is particularly helpful for websites that serve a multilingual audience, offering users a smooth and inclusive search experience. Multilingual machine translation can significantly improve the quality of translations for low-resource languages while maintaining performance for high-resource languages [1].

Better User Experience

One of the biggest perks of using Machine Translation in site search is that it improves the user experience. When users can search in their preferred language, it makes the whole process easier and more user-friendly.

Plus, quick and accurate translations of search results ensure that users get the relevant information they need in a language they understand, which cuts down on the frustration of searching in a foreign language.

Providing accurate translations in site search can significantly enhance user satisfaction and engagement [2].

Wider Reach and Inclusivity

Machine Translation in site search helps websites reach a global audience by breaking down language barriers. This not only makes the search process more inclusive but also creates new opportunities for businesses to connect with customers worldwide.

It can also promote cultural understanding and reduce language-based discrimination by making search results accessible to everyone, regardless of the language they speak.

A study highlighted that the use of multilingual resources in web content has become essential for reaching broader audiences and addressing the linguistic needs of diverse users [3].

Improved Visibility on the Internet

Using Machine Translation in site search can also boost your site’s search engine optimization (SEO). By offering search results in multiple languages, your website can improve its visibility on global search engines, which can lead to more traffic and engagement.

Plus, as search engines like Google focus more on user experience, providing relevant results in different languages can help improve your website’s ranking, making it easier for people to find what they’re looking for.

That said, multilingual search engine optimization can significantly enhance a website’s visibility in global markets [4].

Real-World Examples and Challenges

There are many examples of websites that have successfully implemented Machine Translation in their site search.

For instance, Google Translate, the world’s most popular translation service, allows users to translate entire web pages, making it easier for people to access information in a language they understand.

Ecommerce giants like Amazon and eBay have also implemented Machine Translation, allowing users to search for products in their preferred language.

Challenges to Consider

However, implementing Machine Translation in site search isn’t without its challenges. One of the main challenges is ensuring that translations are accurate and reliable, which can be tricky for languages with complex grammar or specialized vocabulary.

Some websites address this by using human oversight to correct errors in the translations.

Another challenge is keeping up with the constantly changing language and cultural nuances. To tackle this, many websites use a mix of Machine Translation for quick and easy translations and human translation for more complex language needs [7].

Ready to Boost your Site Search Experience?

Adding Machine Translation to site search can greatly enhance the search experience for people who speak different languages.

It breaks down language barriers, making information more accessible.

If you are looking for a reliable site search solution that supports Machine Translation, look no further than LupaSearch.

When users search for the product in their native language, LupaSearch automatically translates the search query into the product catalog language and returns user-relevant results.

This process happens automatically in the backend. Your shoppers won’t see the translated text - only the product recommendation they were looking for, in the language they searched.

Now that’s the search effectiveness and improved user experience, combined.

Book a free product demo at LupaSearch, and significantly boost user experience.

References

  1. Tan, X., Leng, Y., Chen, J., Ren, Y., Qin, T., & Liu, T. (2019). A Study of Multilingual Neural Machine Translation. ArXiv, abs/1912.11625. [1912.11625] A Study of Multilingual Neural Machine Translation (arxiv.org)

  2. Zhang, B., & Misra, A. (2023). Machine Translation Impact in E-commerce Multilingual Search. , 99-109. https://doi.org/10.48550/arXiv.2302.00119.

  3. Mityagina, V., Naumova, A., & Novozhilova, A. (2023). Translatological Grounds of Creating Multilingual Internet Resources. Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije. https://doi.org/10.15688/jvolsu2.2023.3.1.

  4. Eriguchi, A., Xie, S., Qin, T., & Awadalla, H. (2022). Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations. ArXiv, abs/2206.14982. https://doi.org/10.48550/arXiv.2206.14982.

  5. Ha, T. (2020). Multilingual Neural Translation. . https://doi.org/10.5445/IR/1000104498.

  6. Dew, K., Turner, A., Choi, Y., Bosold, A., & Kirchhoff, K. (2018). Development of machine translation technology for assisting health communication: A systematic review. Journal of biomedical informatics, 85, 56-67. https://doi.org/10.1016/j.jbi.2018.07.018.

  7. Fan, A., Bhosale, S., Schwenk, H., Ma, Z., El-Kishky, A., Goyal, S., Baines, M., Çelebi, O., Wenzek, G., Chaudhary, V., Goyal, N., Birch, T., Liptchinsky, V., Edunov, S., Grave, E., Auli, M., & Joulin, A. (2020). Beyond English-Centric Multilingual Machine Translation. J. Mach. Learn. Res. 20-1307.pdf (jmlr.org)