Translation Technology

Debunking Myths of Automated Translation: Separating Fact from Fiction

As the world is moving towards AI and automation in the translation industry, what are the biggest misconceptions about it? We're here to set the record straight and help you choose the right translation solution for your business.

Dominic Dithurbide's avatar
Dominic Dithurbide

June 30, 2023


In the age of technological advancements, automated translation has gained significant attention. However, they have also been surrounded by myths and misconceptions. From speculation about translation output quality to the elimination of human review, find out some of the most common myths about machine website translation tools. 

What is Automated Translation? 

Automated translation is when translation workflows happen without any human intervention. Some examples of automated translation are proxy solutions and the translation mechanism used to translate, for example, machine translation, human translation, AI, MTPE (machine translation post-edit) and more. Automated translation systems are designed to mimic the human translation process by utilizing algorithms and statistical models trained on large amounts of bilingual or multilingual data. These models learn patterns and statistical relationships between words, phrases, and sentences in different languages. Based on these patterns, the system generates translations by selecting the most probable target language equivalents for the given input. 

There are different approaches to automated translation, including rule-based, statistical, and neural machine translation (NMT). Rule-based machine translation relies on predefined linguistic rules and dictionaries, while statistical machine translation utilizes statistical models that analyze patterns in large corpora of bilingual text. Neural machine translation, the most recent approach, uses artificial neural networks to learn patterns and relationships in parallel bilingual or multilingual data. 

Myth 1: Automated Translation Alone Achieves Human-Level Accuracy  

In a survey conducted by CSA Research in 2020, 58% of respondents said that they use machine translation to translate content, while 35% said they use human translation. The remaining 7% use a combination of both. Automated translation systems have made remarkable progress, but they are still far from achieving human-level accuracy. Research conducted by industry experts reveals that the average quality of machine translation is around 65 to 85 out of 100, depending on the language pair and domain. 

So how can you better your machine translation? Since it depends heavily on how it’s trained, you need to make sure you’re using the right tools. Using Neural Machine Translation (NMT), specifically a brand adapted model, makes the process easier and will help produce better results over time.  

Training a neural machine translation (NMT) system for a specific domain involves several steps to ensure the best possible results. Here’s a recommended approach for training a brand domain NMT: 

  • Data Collection: This data should include a variety of text types, such as product descriptions, user reviews, marketing materials, and any other relevant content. Aim for a diverse and representative dataset to capture the nuances of your brand’s language. 
  • Data Preprocessing: Clean and preprocess the collected data to ensure its quality and consistency. This involves removing noise, correcting formatting issues, normalizing text, and aligning sentences in the source and target languages.  
  • Data Annotation: Annotate the collected data with additional information, such as part-of-speech tags, named entities, or domain-specific terminology. This helps the NMT system better understand the specific linguistic characteristics and terminology of your brand domain, enhancing the translation quality. 
  • Model Selection: Choose a suitable NMT architecture for training your brand domain NMT system. Options include attention-based models like Transformer or recurrent neural network (RNN)-based models like LSTM. 
  • Training Setup: Configure the training parameters, such as batch size, learning rate, and optimization algorithms. Training neural networks can be computationally intensive, so ensure that you have sufficient computational resources, such as GPUs, to accelerate the training process. 
  • Training and Evaluation: Train the NMT model on your brand domain data using the chosen architecture and training setup. Monitor the training process and evaluate the model’s performance at regular intervals using appropriate metrics such as BLEU (Bilingual Evaluation Understudy). 

Myth 2: Automated Translation Can Replace Human Translators Completely 

Contrary to popular belief, automated translation cannot entirely replace human translators. According to a survey conducted by Common Sense Advisory, only 19% of respondents were satisfied with the quality of machine translation when it came to business-critical content. This showcases the limitations of automated systems and the need for human translators who possess the linguistic skills and cultural understanding required for accurate and nuanced translations. 

At MotionPoint, we use a technique called Machine Translation Post-Editing. MTPE is the process of reviewing and editing machine-translated content by human translators or post-editors to improve its quality, accuracy, and fluency. MTPE combines the benefits of automated translation technology with human expertise to ensure high-quality translations that meet specific requirements. 

The primary goal of MTPE is to refine and enhance the output generated by machine translation systems. Human post-editors review the machine-translated text and make necessary edits to correct errors, improve the grammar and syntax, ensure consistency, and make the translation more natural and fluent. They also take into account context, cultural nuances, and domain-specific knowledge to produce a final translation that meets the desired standards. 

Myth 3: Automated Translation is Costly  

According to a study by the European Union, machine translation can reduce translation costs by up to 80% compared to human translation. Automating a large portion of your translation needs can actually REDUCE your translation costs overtime! This is for a number of reasons: 

  • Increased Efficiency: NMT systems automate the translation process, allowing for faster turnaround times, which can lead to cost savings. 
  • Reduced Manual Effort: NMT reduces the manual effort required for translation by automating the initial translation step, leaving human translators to focus their efforts on post-editing and fine-tuning. 
  • Consistency and Reusability: NMT systems produce more consistent translations by following predefined patterns and linguistic rules learned during training, which eliminates the need for repetitive translation work for recurring phrases or terminology. 
  • Scalability: NMT systems can handle large volumes of text efficiently, making them suitable for scaling translation operations. As the amount of content increases, NMTs can handle the workload more easily than traditional human translation. 
  • Translation Memory Integration: NMT systems can be integrated with Translation Memory (TM) systems, which store previously translated segments or phrases. This reduces the amount of new content that needs to be translated and lowers overall costs. 

In a survey conducted by SDL in 2020, 88% of respondents reported that using translation memory technology helps them achieve more consistent translations, while 68% reported that it helps them reduce translation costs. 

Myth 4: Automated Translation is a Black Box 

One of the greatest misconceptions about proxy translation is that it’s a “black box” and you can’t see or manage any of the work. With some technology-only approaches to proxy, you are responsible for implementing, setting up and operating the solution on your own. This requires a large development team, along with the management of complex processes and workflows. While this is a good option for having control over the whole process, it isn’t practical for most businesses. 

With MotionPoint, not only do you not have to handle the process of translation and management, but we don’t hide anything. Transparency is crucial in proxy translation to improve user experience, brand consistency, SEO and search visibility, content control, and security. With MotionPoint’s Control Center, the myth about proxy being a black box couldn’t be less true. You have visibility into where in the translation process your content is. You have the ability to submit content to be translated, as well as make edits and suggestions before approving everything. Our team is highly responsive and committed to making your experience one that goes beyond just the words. 

Learn all about the advantages of the right proxy approach, like visibility into the site and keyflow monitoring, ongoing SEO efforts, and new content detection and deployment. 

Don’t Get Fooled by Myths 

Automated translation has undoubtedly made progress, but the myths surrounding its capabilities persist. Many still think of automated and machine translation as costly and insufficient in quality. The truth is the use of machine translation actually has the ability to lower costs over time, and using proper technology in combination with human post-editing can produce human-quality translations. 

Don’t let these myths discourage you from engaging in automated translation. Let the technology experts at MotionPoint guide you through automated translation. We use tools like algorithmic translation, translation memory, and AI in our Adaptive Translation™, with optional human post-editing, to debunk these machine translation misconceptions. Drop us a line today to learn more! 

Last updated on June 30, 2023
Dominic Dithurbide's avatar

About Dominic Dithurbide

Dominic Dithurbide is a creative, goal-driven marketing leader that’s dedicated his career to the translation industry. Dominic brings proficiency in global marketing, demand generation, and go-to-market strategies to MotionPoint’s marketing team.

Dominic Dithurbide's avatar
Dominic Dithurbide

Marketing Manager


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