What is AI Translation?
We’ve been surrounded by AI for a while now: the PS you bought in 2010 to get you across the country, the robot vacuum that roams your kitchen, and the facial recognition you use to get into your phone. We sometimes think of AI as an undefinable, complicated super-tool. It doesn’t always have to look like JARVIS from Iron Man, building suits and controlling an entire home. AI helps us all do our jobs better, especially in the translation industry. Artificial intelligence lives in your translation management system to detect and reuse patterns in your content.
The Evolution of AI
Artificial Intelligence (AI) has a history that dates back to the 1940s, but it wasn’t until the 1950s and 1960s that AI research began to take off. Early AI research focused on developing algorithms that could solve problems using symbolic reasoning, but progress was slow due to limited computing power.
In the 1980s, a new approach to AI called “machine learning” emerged, which allowed algorithms to learn from data instead of being explicitly programmed. This approach led to breakthroughs in areas such as speech recognition, computer vision, and natural language processing.
In the 1990s and 2000s, the rise of the internet and the availability of large datasets fueled the development of machine learning algorithms that could handle massive amounts of data. This led to the development of deep learning, a subset of machine learning that uses neural networks to learn hierarchical representations of data.
In recent years, there has been a renewed interest in AI, fueled by advances in deep learning, the availability of powerful computing resources, and the growth of big data. Now in the 2020s, AI tools like ChatGPT are becoming widespread. ChatGPT is being adopted and tolerated as a machine translation tool. As the use cases for artificial intelligence continue to evolve, the quality and accuracy of translations will only get better.
How Can AI Benefit Businesses Looking to Translate Content?
AI translation has quickly developed congruently with other general AI technology. The global investment in artificial intelligence grew from $12.75 billion in 2015 to $67.8 billion in 2020. Similarly, the machine translation market is continuing to grow by approximately 19% every year.
AI translation can help businesses achieve their goals more easily. By automating translation, businesses can:
- Reach a Wider Audience: With the help of AI translation, businesses can easily translate their content into multiple languages, making it accessible to a wider audience. This can help businesses to expand their reach and increase their customer base.
- Save Money: AI translation reduces human translation costs, typically the most expensive part of website translation.
- Get Faster Turnaround: AI translation can be done much faster than human translation, allowing businesses to quickly translate their content and respond to market demands in a timely manner.
- Serve Customers Better: By translating their content into multiple languages, businesses can provide a better customer experience for their multilingual customers. Customers want to see product information in their own languages; 76% of customers prefer to shop in their own language, while 40% will not buy from a site in another language. This can help to build trust and loyalty among customers.
- Gain Competitive Advantage: Businesses that are able to offer content in multiple languages have a competitive advantage in global markets. AI translation can help businesses to quickly and easily translate their content, giving them an edge over competitors who may not be able to offer translations.
In summary, AI translation can help businesses to expand their reach, save money on translation costs, improve efficiency, and provide a better customer experience. This can ultimately lead to increased sales and a stronger position in the global market.
Learn about how Adaptive Translation™ can get you all these benefits
Types of AI Translation Technologies
Statistical Machine Translation (SMT)
Statistical Machine Translation (SMT) is a type of machine translation that uses statistical models to translate text from one language to another. SMT works by analyzing large amounts of bilingual text to learn how words and phrases in one language correspond to those in another language.
SMT typically consists of three main components:
- Translation model: This component uses statistical models to determine how words and phrases in one language correspond to words and phrases in another language. The translation model is trained on a large corpus of bilingual texts, which allows it to learn the statistical patterns of language use.
- Language model: This component generates the target language text by predicting the most likely sequence of words based on the source language input. The language model is trained on monolingual text in the target language, allowing it to learn the probability of different word sequences in that language.
- Decoding algorithm: This component selects the best translation output from a set of possible translations generated by the translation and language models.
SMT has several advantages over other machine translation approaches, such as rule-based machine translation. SMT does not require expert knowledge in linguistics, and it can adapt to new domains and language pairs with minimal human intervention. SMT is also able to handle a wide range of text types and genres, from technical manuals to literary works.
However, SMT has some limitations. It can struggle with translating idiomatic expressions, metaphors, and other nuances of language. It also requires large amounts of bilingual data to train the models, which can be difficult to obtain for less commonly spoken languages or specialized domains.
In recent years, SMT has been largely superseded by neural machine translation (NMT), which uses deep learning techniques to improve translation quality. However, SMT remains an important approach to machine translation, especially for low-resource languages and specialized domains where large amounts of training data may not be available. Learn more below about how MotionPoint uses NMTs to create the most accurate and well-rounded translations possible.
Adaptive Translation™: How MotionPoint Uses AI
MotionPoint’s new Adaptive Translation™ model combines translation memory, algorithmic translation, and artificial intelligence to deliver human-quality translation at a fraction of the cost. Our AI translation strategy involves using Neural Machine Translation to optimize outcomes.
Neural Machine Translation (NMT) is a state-of-the-art machine translation approach that uses deep neural networks to translate text from one language to another. NMT differs from Statistical Machine Translation (SMT) in that it uses neural networks to learn the mappings between languages, rather than relying on statistical models. During training, NMT models are trained on large amounts of bilingual text data using a process called backpropagation. The model learns to optimize a specific objective function, such as maximizing the likelihood of the correct translation given the source language input.
NMT requires large amounts of training data and computational resources, which can be a limitation for low-resource languages or smaller organizations with limited computing resources. NMT also struggles with rare or unknown words and can sometimes generate translations that are ungrammatical or semantically incorrect.
In recent years, NMT has become the dominant approach to machine translation, and it has achieved state-of-the-art performance on several benchmarks. Researchers continue to explore new architectures and techniques to improve the performance of NMT models, such as incorporating external knowledge sources or leveraging unsupervised learning methods.
Generic NMTs are basic AI translation tools, like ChatGPT, that produce basic translated sentence and word pairs. While the outputs may be mostly accurate, they are considered below standard because they are simply word-for-word and lack customization. However, they tend to be a less expensive option.
Brand Adaptive NMTs
Brand-Adaptive NMT models are trained and customized to each customer’s brand voice and preferences. It uses existing translation memory data, domain-relevant datasets, and our proprietary data processing pipeline to provide you with custom translations that better match your brand’s glossary in other languages.
Pros and Cons of AI in Translation
Using AI translation boasts some pretty important benefits for companies looking to translate quickly and accurately.
- Speed: One of the main advantages of using AI in translation is that it can work much faster than human translators. This means that large volumes of text can be translated quickly and efficiently, which can be useful in a variety of situations, such as translating legal documents, technical manuals, or website content.
- Cost: AI translation can be more cost-effective than hiring human translators, especially for large translation projects. While there is a cost associated with training the AI system, once it is trained, it can work 24/7 without needing breaks or payment for overtime.
- Consistency: AI translation can provide more consistent translations than human translators. Since AI systems follow a set of rules and algorithms, they are less likely to make errors or deviate from the intended meaning of the text.
AI in translation has some amazing benefits, but it doesn’t come without its limitations.
- Quality: While AI translation has improved greatly in recent years, it still cannot match the quality of a human translator, especially when it comes to more nuanced or creative translations. This is because AI systems often struggle with context, idiomatic expressions, and other subtleties of language.
- Cultural Differences: Language is not just about translating words, it’s also about understanding culture, customs, and values. AI systems may not always understand these nuances, which can lead to translations that are not appropriate or accurate for the target audience.
- Limited languages: Not all languages are supported by AI translation systems, and even for those that are, the quality may vary depending on the language pair. This can limit the usefulness of AI translation in certain regions or for certain languages.
Overall, AI translation has the potential to greatly improve the speed, consistency, and cost-effectiveness of your projects. However, it may not be suitable for all types of translations, and the quality may not match that of a human translator, especially for more complex or creative translations.
Learn about the differences between human and machine translation
Will Human Translation Still Be Relevant?
AI translation helps to streamline and optimize so much of the website translation process, but human translation and editing will always be necessary to not only catch mistakes, but also to add an element of localization. AI cannot provide a human factor, but it makes the job of a human linguist easier and helps translation companies provide better quality at a lower cost to the customer.
Human translators can actually benefit from AI translation assistance by using tools that provide real-time suggestions and edits. Some AI tools are also able to offer metrics on translated texts that analyze the quality of the translation. It’s clear that combining the benefits of both human and machine translation will lead us to the best outcome possible.
Learn how you can leverage AI translation tools by partnering with MotionPoint