Enhancing Translation Accuracy: Exploring the Context Translation Function

In the era of globalization, effective communication across language barriers has become increasingly crucial. Machine translation tools have emerged as valuable aids in bridging this gap. Our translation company, Tomedes, recently launched a machine translation comparison tool under Machinetranslation.com. We have created a new beta feature called Context Translation that will make generated translation more personalized. 

Today, we will explore what makes this translation tool different from others and how it has implemented GPT technology to compare and analyze the source and target language of the machine-translated output. Also, we will discuss how our context translation feature can help users create a translation that’s tailored to their needs, which will be helpful to businesses, content creators, and translators. 


Our Context Machine Translation Function

Previously, our translation tool at Machinetranslation.com only allowed the comparison of machine translation engines, such as Google Translate, Amazon Translate, DeepL, ModernMT, and ChatGPT. Through GPT technology, our machine translation tool could simultaneously generate translated content across the mentioned machine translation engines and create analyses from the different outputs. We created this tool to make it easier for translators to identify which machine translation engine will be suitable for their projects, as some machine translation engines are more reliable for certain languages than others. Content creators and businesses can also use this tool if they ever plan to implement machine translation as part of their operations. But really, anyone can use this tool. 

Before we dive into our newest Beta feature, let's first discuss what context translation is. Context translation is the process wherein the meaning of the source text is taken into consideration, and ensuring that the meaning resonates in the text of the target language. 

The context translation aspect provides a set of AI-generated questions to make the translated output more fitting for any given conversation. Depending on the content, our tool will generate questions based on AI-identified aspects that the user likely wants to be tweaked – the overall tone, the primary audience, overall treatment (literal or creative), and translation variants. 


How Does Our Comparator Tool Function?

The comparator tool within Machinetranslation.com allows users to compare the translations generated by various neural machine translation engines, such as Google Translate, Amazon Translate, and DeepL. The tool provides a side-by-side comparison of these engines, and users can evaluate the quality and accuracy of translations, enabling them to make informed decisions about which machine translation engine to utilize. 

We wanted our users to be knowledgeable of the various free neural machine translation engines in the market and, through the comparisons and AI-generated analysis, they can learn that no two engines give the same results and that some engines are specialized to certain language groups. We added our Context Translation Beta feature alongside our comparison translator tool because we wanted to provide options to our users and break the stereotypes that machine translations are unsophisticated and lack accuracy, which is true to an extent but the technology has greatly improved over the years.


Is this Feature Available on Other Platforms?

We are continuously updating and improving our machine translation tool. So far, contextual translation with the use of machine translation has mainly relied on human translators to personalize the content and translate the text based on the context of the message. However, what we are trying to do with our platform is to have AI produce contextual questions based on their preferences, the source text, and the language pair. Some platforms are also experimenting with similar features to improve translation accuracy, and the specifics of the functionalities of their tool will vary from ours. That is why we are encouraging users to send their feedback so we can identify areas that need more improvement.


What Experts Think of Context Translation

To gain deeper insights into the efficacy of the Context Translation function, we reached out to professional translators and experts in the field. Their feedback has been overwhelmingly positive, with many highlighting the substantial improvement in translation quality achieved through context-based translation. The ability to grasp the nuances of the source text and provide contextually appropriate translations that are personalized to their preferences and needs has greatly impressed the experts, elevating the overall user experience.

However, as much as we appreciated the support and positive feedback that we received, we're going, to be honest, there was a lot of constructive criticism that we took to heart.

For example, one native Samoan translator told us they were disappointed that the tool didn't have their native language. Low-resource languages like Samoan lack digital content and high-quality sources, which can lead to issues in accuracy and biases and machine translation systems not being able to support the language. Because of this, we are working towards increasing the number of languages supported under our AI-powered Context Translation feature.

Another criticism that we received was that it took some time for the generated analysis of the comparison tool and context translation feature. Since the context translation feature is processed through OpenAI, and as such there were noticeable delays in the process. But moving forward, we plan to find ways to increase the speed as much as possible.


Future Developments

At Machinetranslation.com, continuous improvement is at the core of our mission. We are dedicated to refining the Context Translation function to further enhance its accuracy and reliability. Currently, our AI development team is working diligently to incorporate advanced machine learning algorithms and neural networks, ensuring even more precise translations that capture the intended meaning effectively.

We hope that our Context Translation feature will bring a new level of accuracy and contextuality to machine translation. Considering the surrounding context significantly improves the quality of translations compared to traditional word-by-word approaches. We will continue to invest in research and development, users can expect even more refined and accurate translations, making cross-lingual communication easier and more reliable than ever before.