Advancements in technology have made a huge impact on online translation. Learn more about the interaction between AI and human translators online.
With the help of machine-learning capabilities such as parallel datasets, the level of Artificial Intelligence (AI) in modern-day translation tools competes with human translators. As AI keeps advancing, people will start acting as translators of tone, culture, and emotion. Recently, Microsoft indicated that their machine learning research team had reached a level equal to that of “human translation quality.” So, will AI replace a human translator online?
What Is The Quality of Neural Machine Translations?
The age of neural machine translation (NMT) and AI have transformed the way translations in different language pairs are handled. However, even the Microsoft research team can’t comprehensively claim that their NMT quality is equivalent to that of humans. Their claim only applies to the way news articles are translated from Chinese to English. The same is not true for other language pairs.
NMT tends to focus on sentence structures, not context. In some cases, low-quality human translations can be used as references for assessing the quality of machine translations. NMT seems to deliver credible results when used under artificial conditions where the context of a document is controlled.
How AI Has Been Used in Translation
Currently, translation agencies and translators online are using AI to execute repetitive tasks when translating projects. Most translators use NMT by post-editing and proofreading machine translations to make work easier and consistent. CAT tools that use AI can carry out routine translation tasks such as:
- Relying on previously approved translated content to produce vocabulary suggestions and typing predictions
- Automatically inserting sentence fragments and “fuzzy matches” with an accuracy of up to 100%
- Replacing project managers in most complex localization workflows
- Estimating the quality of localization
- Helping project managers analyze the resource workload and optimize the pool of linguists
- Helping project managers select the right translator for a particular project
- Integrating NMT engines into CAT software through API
How AI Will Change the Work of a Human Translator Online
Soon, NMT is expected to change the way linguists and translators do their work. According to a concept of Augmented Translation developed by the “The Common Sense Advisory” team based in Massachusetts, Augmented Reality (AR) will give translators access to the relevant information they need for translation. Future technologies such as automated content enrichment (ACE), terminology management and adaptive NTM are expected to be very beneficial to translators.
Due to the massive amount of training data that is required to make NMT systems more efficient, it may take some time for NMT to have an actual impact on translation. The main role of AI in translation is currently viewed as the application of Deep Learning in NMT.
As open-source project vendors keep integrating NMT into CAT tools, more and more translator workflows will also start shifting to post-editing. AI is also expected to play a vital role in the translation quality assurance tools and cleaning of NMT training data.
Benefits of Adaptive NMT and ACE
The adaptive NMT technology relies on what the translator feeds the system. In other words, it automatically learns the translator’s writing style and terminologies to help the translator with future translations. This means that translators can influence the type of suggestions a translation tool provides.
ACE enables a translator to find relevant information on ambiguous terms and localize content based on the subject culture. There is a strong connection between ACE and terminology management technology.
Based on the concept of “Augmented Translation,” an online translator doesn’t have to look up for a specific terminology when translating. Meaning they don’t have to scour the internet or use dictionaries to find the meaning of previously used terminologies.
Translators will be able to influence the suggestions provided by NMT systems. The metadata and terminology systems are expected to become more popular in the future as localization keeps growing.
Machine learning and AI will be used to enhance localization workflows to improve business efficiencies. This will help create a more intelligent way of executing localization tasks by spending less on resources.
How AI Has Broken the Language Barrier
Today, AI translation is widely used on various platforms such as Google’s international pages and Facebook feed. Through Microsoft’s Translator App, a translator can run the app offline, especially when traveling in areas with unstable internet connectivity. The app uses AI to translate text, images, speech, and street signs. This is a huge breakthrough when it comes to breaking language barriers and making translation processes easier.
Recently, Facebook refurbished its translation system to introduce AI as their primary translation technology. Unlike in the past, when the translation system used to translate phrases, the AI-based system is now capable of translating the context of an entire sentence for more accurate results. The site relies on user input through their “rate this translation” function to update their neural network in real-time.
Limitations of AI
Regardless of the amount of training data and Deep Learning an AI-based CAT tool gets, a neural network cannot be as effective as a human translator. Meaning, it is not practical to equate the translation quality of an experienced translator to that of an AI translation.
In a recent South Korean contest, a team of professional translators compared a human translation to a 50-minute long of NMT text. According to VentureBeat, the test results, from the Korean to English translation and vice versa, revealed the NMT text was grammatically awkward. This goes to show that an AI translation cannot be likened to a human translation.
Likewise, according to most online reviews about Google Buds’ translation functionality, their AI translations were too imperfect to be of any help to users.
While AI can help a user get a quick translation of short sentences on the go, professional translations for personal or commercial use should be left to human experts. However, this is not to say AI translation is useless.
To fully leverage the benefits of NMT, a translator should combine both human and AI translation. Depending on the type of content