Read on to find out the answers to the most common frequently asked questions by our customers on this topic.
Artificial Intelligence (AI) is the creation of computer systems that possess the ability to undertake tasks that would typically be done by a human. AI encompasses various key components and techniques that drive its functionality.
Here are three relevant ones to the translation industry:
These components form the foundation of AI and enable its applications across various industries, revolutionizing how tasks are accomplished.
AI in translation refers to the use of Artificial Intelligence technologies, particularly machine learning and natural language processing, to automatically translate text or speech from one language to another. The goal is to create automated translation systems that can accurately and fluently convey the meaning of content across different languages without human intervention.
Compared to traditional machine translation that relies on statistical or rules models, systems powered by AI:
On its own, AI is a fundamental component of neural machine translation. It empowers machines to interpret and convert text and audio from one language to another using natural language processing, relying on advanced algorithms and data-driven models.
At the same time, the AI translation systems further incorporate cutting-edge neural networks and deep learning methodologies to augment their understanding of context and language structure, ensuring translation accuracy and fluency.
Large Language Models (LLMs) like ChatGPT have transformed the automated localization process and can be useful for translating generic content with relative accuracy. However, using LLMs for translation has specific disadvantages compared to AI / neural machine translation: lack of specialization and contextual understanding; inconsistencies in the use of glossaries, styles, or colloquialisms; weak quality control; introduction of “hallucinations” (coherent and grammatically correct translations that are factually incorrect or nonsensical); and a shortfall in the handling of cultural nuance. LLMs are however rapidly evolving so it won’t be surprising to see them being integrated more closely into AI / neutral machine translation solutions in the near future.
AI translation systems continuously learn and improve their performance as they process more data. They can be deployed for various applications, including:
Costs for translating your content using an advanced AI translation system vary by language. Typically, costs are associated with preparing the content for AI translation, integrating it with the AI translation engine, and generating the AI-translated content.
You can choose to release documents translated only with AI with a disclaimer if they are only for information purposes. However, AI-translated content is usually not suitable for professional, customer-facing publishing without first doing some post-editing.
Post-editing is the correction of AI-translated text to ensure that it meets the "agreed level" of publishable quality requested by the client. There are two levels: Full post-editing (similar to human translation output) and light post-editing (focusing only on accuracy, fluency, syntax, and grammar). Post-editing is not the same as editing. Editing is the process of correcting human-generated text.
Post-edit or post-editing distance is the percentage of changes done by a linguist in a text after AI translation has been applied to it, to generate content that complies with the expected level of quality in terms of accuracy, fluency, syntax, and grammar.
The cost of post-editing AI-translated content depends on the number of words to post-edit, the level of post-editing expected, and how much time it takes to fix it.
AI translation can handle any type of file if provided in a localization-friendly format. These include:
SimulTrans can also handle virtually any other file format by pre - or post-processing them.
Book a paid consultation with an AI localization expert to discuss how to integrate AI as part of your localization process.
AI translation works well for localizing most content types, although it is not recommended for highly creative marketing copy. This is because copywriting often relies on cultural context to convey its message. Types of content better suited to AI translation include documentation, communications, descriptive text, user interface, help and knowledge bases, and general legal, medical, and multimedia content. Check out if your content is suitable for AI translation with our free machine translation suitability report.
The most advanced engines are compatible with more than 100 languages, from Albanian to Zulu. They can also handle source content in a limited set of languages, besides English.
SimulTrans can accommodate almost any timeline with AI translation. Applying AI translation to the content does not take long, but if your project requires post-editing, timelines depend on word count, content complexity, and the number of linguists involved. Post-editors can usually handle about 7,000 words per day. Our linguists work simultaneously, so the number of target languages won’t impact your timeline.
Several automated quality scores or indicators have been traditionally used to provide quality measurements for machine-translation output, like BLEU, TER, or METEOR. However, these are obsolete now because of the substantial improvement in AI translation output.
Translation quality assessment relies on AI-powered features that have been recently developed to estimate the translation quality. These features are based on algorithms that can predict how much post-editing will be required. Human translators can also evaluate the quality by comparing translations from different engines to decide which one provides the best output for the specific content.
AI-powered applications have become ubiquitous in various industries, including healthcare, finance, manufacturing, transportation, and more. These technologies have made remarkable progress in the field of machine translation, but it's crucial to acknowledge their limitations, particularly when dealing with highly specialized or context-dependent content. That's why human translators play a vital role in reviewing and refining the output to ensure accuracy and capture cultural nuances.
If you're eager to delve deeper into the world of AI translation or learn more about the use of machine translation, don't miss out on our free eBook: Machine Translation and Post-Editing: Six Steps to Success. Download it now to unlock a wealth of knowledge and insights.