Most people are familiar with Google Translate (GT). In fact, you might\u2019ve even used it when traveling or translating a document sent from your foreign colleague.<\/p>\n\n\n\n
GT and other modern tools can churn out amazingly accurate translations as if a human created them. That technology opens up a new realm of powerful possibilities in marketing, localization, business expansion, and other applications.<\/p>\n\n\n\n
This article will cover the accuracy of machine translation, how far it has come, and some expert advice for improving your results.<\/p>\n\n\n\n
Before you can do a machine translation quality assessment, it helps to know the types of translators available, as this knowledge will help you make informed decisions about evaluating and improving translation output.<\/p>\n\n\n\n
Rule-based translation is the oldest technology used by some of the first machine translators. It applies pre-defined linguistic and syntax rules to guide its decision when translating between languages. Language experts usually help define these rules.<\/p>\n\n\n\n
The rule-based approach is simplistic and technically easy to implement, making it pretty widespread during the early days. However, it poses several limitations, such as low scalability, the inability to handle nuances like idioms, and a struggle to adapt to varying contexts.<\/p>\n\n\n\n
Thus, the rule-based method is used where understanding the general meaning of a translation is prioritized over its accuracy. Informal communication when traveling to another country is a good example of how this technology helps with certain scenarios.<\/p>\n\n\n\n
Statistical translation uses a data model filled with the correspondences of words and phrases between two languages. Think of it as an English-Spanish dictionary, for example. The algorithm then learns patterns in this data to translate it successfully.<\/p>\n\n\n\n
This approach means that the statistical method can consider language nuances and constructs like idioms and translate them successfully. It\u2019s also great for technical translations, such as engineering and law.<\/p>\n\n\n\n
The statistical translation dramatically improves quality compared to its rule-based predecessor. However, its biggest drawback is that it requires a large amount of parallel data to achieve decent quality.<\/p>\n\n\n\n
Neural machine translation uses machine learning and artificial intelligence (AI) to interpret. It\u2019s considered the most advanced and accurate approach today, thanks to the technology\u2019s ability to adapt and improve. <\/p>\n\n\n\n
One huge leap for neural machine translation is its ability to understand nuances and complex language constructs like idioms, expressions, and metaphors. That leads to almost human-like translations, especially if paired with generative AI platforms. Best of all, neural machine translators can improve over time, so their accuracy will only increase.<\/p>\n\n\n\n
As you can see above, machine translation has undergone several iterations and technological changes. And because of this, machine translation quality has also improved dramatically. In fact, it\u2019s pretty accurate compared to human translation in most situations.<\/p>\n\n\n\n
A study by Weglot<\/a> set out to prove this theory. They asked human translators to translate a sample text on major platforms like Google Cloud Translation and Amazon Translate. The result was that human translators considered 85% of the samples as acceptable. At most, it only required minor editing by a human editor.<\/p>\n\n\n\n Such a result is considered good enough for everyday situations. For example, it makes it possible to understand someone speaking another language. However, you must also consider that translation quality depends on several factors.<\/p>\n\n\n\n The first factor is known as the language pair<\/strong>. Some words are easier to translate from specific languages compared to others. For example, Google Translate has a 94% accuracy<\/a> when translating English words to Spanish, which is a widespread language. Contrast that with a less popular language pair like English-Armenian, and the results drop to 55%.<\/p>\n\n\n\n Another factor that affects machine translation accuracy is context<\/strong>. Simple, everyday phrases with common words and phrases are more straightforward to translate and are, therefore, more accurate. Complex language constructs like idioms and jargon can be difficult for machine translators to convert, leading to a lower-quality translation.<\/p>\n\n\n\n The third factor is the domain<\/strong> of the words. Translations in highly advanced and technical topics like engineering or the sciences can be challenging for machine translations. For example, a study by the UCLA Medical Center<\/a> found that translation accuracy in their field went as low as 55%.<\/p>\n\n\n\n Lastly, the platform <\/strong>itself matters. Some machine translators are much more accurate than others, such as Google Translate\u2019s claim that it can achieve 94% accuracy<\/a>.<\/p>\n\n\n\n Despite these variables, machine vs human translation accuracy has improved to the point that it does a great job on most everyday usage. Even if it doesn\u2019t, it only requires a human editor to perform minor fine-tuning to make the results top-notch.<\/p>\n\n\n\n Why should you care about the accuracy of your machine translator? When done right, machine translation can offer a host of benefits, including:<\/p>\n\n\n\n Machine translation can help businesses quickly process large volumes of documents, audio, and video. You can simply upload a file into the software with just a few clicks. In contrast, it takes significantly longer for a human to handle the same amount of translation.<\/p>\n\n\n\n Plus, modern machine translators are fast enough to achieve real-time translations. For instance, this lets international branches talk with each other without the need for an interpreter. That enables seamless global collaboration.<\/p>\n\n\n\n Machine translations are incredibly versatile and can help businesses in various ways.<\/p>\n\n\n\n For instance, it can be an invaluable marketing tool. Teams can get an English video and translate it effortlessly into multiple languages. That allows you to repurpose content to reach a wider audience without re-shooting a localized version.<\/p>\n\n\n\n Another application of machine translation is employee training. Depending on the employee’s background, management can easily offer training materials in another language. That lets your staff absorb the information faster, making them more effective team members.<\/p>\n\n\n\n One of the critical challenges of business expansion, especially into foreign markets, is the language barrier. Machine translation eliminates this obstacle, so you can focus on growing your enterprise.<\/p>\n\n\n\n For instance, machine translation can enable ecommerce stores to localize their product descriptions, listings, and websites quickly. That lets them tap into a new market without spending weeks hiring an interpreter to create a regional website.<\/p>\n\n\n\n Machine translation can also be used for market research<\/a>. Transform local ads, videos, and other files into your native language so you can analyze the data.<\/p>\n\n\n\n Perhaps the most significant advantage of machine translation is its cost. It opens up the power of translation to just about anyone.<\/p>\n\n\n\n Not too long ago, you\u2019d need to hire a human translator<\/a> or interpreter to convert a video or audio from one language to another. A decent provider would charge around 15 \u2013 30 cents a word, costing about $150 – $300 for a 1,000-word video script. That\u2019s not economically viable, especially when you consider the alternative.<\/p>\n\n\n\n A machine translator, in contrast, gives you comparable results for significantly less money. For example, the Sonix platform will transcribe and translate an hour of video for just $10.<\/p>\n\n\n\n Now that you\u2019ve learned how to measure the accuracy of machine translation outputs, the next step is knowing how to improve the results.<\/p>\n\n\n\n Start by using simple language as much as possible. That means using shorter sentences with a simple structure. Avoiding compound words, run-on sentences, expressions, and jargon can help make translations smoother and more accurate.<\/p>\n\n\n\n It\u2019s also worthwhile to find the right translator for your needs. As we mentioned above, some translators are more accurate than others. Furthermore, some platforms are made for specific language pairings. Try experimenting with different options and see which one yields the best results.<\/p>\n\n\n\n If possible, ensure your translation model is trained on a high-quality dataset. You can also try feeding it samples related to the output you\u2019re looking for to improve accuracy.<\/p>\n\n\n\n Of course, it\u2019s unfair to expect machine translation to achieve 100% accuracy. Thus, the best way to achieve high accuracy is through post-editing. Having a human editor correct errors, improve fluency, and adjust grammatical hiccups will improve overall results.<\/p>\n\n\n\n Machine translators have come a long way in recent years. You may have experienced some clumsy results a few years back, but it can now produce amazingly human-like automated translations<\/a>.<\/p>\n\n\n\n However, your experience will depend on a few critical factors, such as the translation tool you use. And if you\u2019d like the best, most accurate results, you\u2019ll get them using Sonix\u2019s AI transcription<\/a> software.<\/p>\n\n\n\n Sonix is a transcription and translation tool rolled into one. Using advanced AI algorithms, it can accurately translate your audio or video file into 40+ other languages \u2013 all with just a few clicks. Plus, powerful editing and captioning tools let you optimize the translations, helping you achieve affordable and professional results quickly.<\/p>\n\n\n\nThe Benefits of Using Machine Translation<\/h2>\n\n\n\n
Speed<\/h3>\n\n\n\n
Functionality<\/h3>\n\n\n\n
Ability to Scale<\/h3>\n\n\n\n
Cost Effective<\/h3>\n\n\n\n
Tips for High Translation Accuracy<\/h2>\n\n\n\n
High-Quality, Accurate Translations from Sonix<\/h2>\n\n\n\n