AI Agents for Language Translation

Posted by seomypassion12 on November 1st, 2023

Language diversity and dialects present a major challenge in customer experience management. AI translation solutions are redefining conversation channels, increasing agent efficiency, and reducing the need for human translators and polyglots.

This new approach to low-resource translation uses an intelligent system that mimics the roles and interaction of a human translation team. It includes a translator, back-translator, evaluator and possibly a project manager.
1. Faster Translations

Unlike traditional translation tools, Taskade Video Conferencing AI agents for language translation use neural machine translation (NMT) and natural language processing to analyze context and meaning at scale. NMT technology is trained on a large amount of bilingual data to understand the patterns and nuances of language so it can produce a more natural translation and better contextually match words and phrases to their meaning in a new language.

NMT allows global brands to localize content quickly and easily while ensuring consistency and accuracy. This means that businesses don’t have to factor in the time it takes to find skilled translators when calculating lead times, and can deliver content to market much faster.

This speed is also augmented by the ability to perform automated editing and quality checks as content is being translated. This removes a step from the translation process and frees up translators’ time to work on other projects. Automated editing can identify errors and highlight them for corrections. It can even suggest changes to existing content and help authors improve future translations.

The benefits of leveraging AI to assist with translation go beyond the obvious speed gains. Effective localization allows for more accurate communication with international audiences, and can positively impact a brand’s reputation and performance.

A simple example of this is the HSBC campaign “Assume Nothing,” which was misinterpreted in China as “Do Nothing.” This resulted in a backlash against the company and its brand. AI-assisted translation can prevent these types of blunders by ensuring that brand messages are conveyed accurately in each market and region.

Language AI is the industry’s premier translation platform for intelligent voice, text and document localization. Built on Microsoft Translator API, this powerful translation engine powers billions of translations daily in Bing and Office. Powered by NMT, it enables fast, high-quality machine translation and can be customized for domain-specific terminology to ensure consistent branding across languages.
2. Accurate Translations

Artificial intelligence has made significant advancements in many areas, but it's still not yet capable of understanding nuanced language like a human brain. As a result, AI translation systems can struggle with idiomatic expressions, cultural references, and wordplay that require interpretation.

Additionally, the quality of a machine translation depends on how well it is trained, and that could lead to biases that aren't apparent in the original content. This is especially problematic when working with sensitive or politically charged material.

Despite these limitations, AI-powered translation holds great promise for the customer experience industry. It can reshape conversation channels by empowering agents to connect with consumers in their native tongue, and reduce the need for specialist translation talent.

However, AI isn't quite ready to break down the barriers of language and replace skilled human interpreters and translators in high-stakes conversations. It may also not be able to translate everything just yet, particularly low-resource languages that aren't well represented in training data.

Fortunately, translation is getting better and faster as AI technology evolves. New advances in neural machine translation (NMT) make it possible to generate more accurate translations than ever before. NMT uses deep learning algorithms to determine how to translate content by interpreting the meaning of the source text. It can then provide a more natural-sounding translation that is close to the original in meaning and tone.

NMT has a variety of applications across industries, including legal and insurance, as well as healthcare and hospitality. For example, an insurance company could use NMT to help a client understand their policy, or a hospital can implement it in their customer service center to facilitate communication with patients. One way to leverage NMT is by using a translation management system, such as Memsource, which makes it easy for companies to roll out translations and track their quality over time.
3. Reduced Errors

Incorporating language translation into a customer service process allows your business to connect with customers and employees in their native languages. This can help you reduce costly and frustrating errors caused by miscommunication.

AI tools for translation are getting increasingly sophisticated, allowing them to catch more errors than human translators could before. However, it’s important to keep in mind that even the best AI translation systems still don’t come close to matching the power of the human brain, so a team of skilled linguists will always be necessary for high-stakes scenarios like marketing campaigns or legal documents.

The good news is that while MT errors can be embarrassing and damaging, they tend to happen less often than most people realize. Many businesses use MT-powered tools to provide rough translations that are then edited by a professional linguist, so you’ll likely only see them if something goes horribly wrong. In most cases, a mistranslation will result in a laughable error message that isn’t serious enough to warrant a response from the company.

One of the main challenges with machine learning is that it can’t account for context when processing a new piece of information. For example, if you’re talking to someone on the phone in French and they respond in English, the AI may translate that into “I’m sorry, I don’t understand.” While this error is funny at first, it can be extremely annoying for your caller and damage the brand’s image.

In addition to reducing errors, incorporating translation into your customer service processes can also improve inclusivity. This is because it can help you avoid using biases, slang, and discriminatory expressions that could be offensive or insensitive to your audience. This will allow you to build a stronger relationship with your customers and create a more diverse workplace.
4. Automated Subtitling

The simplest way to automate the translation process is with automatic speech recognition (ASR). This technology is transforming how video content is captioned by creating automated transcriptions that can be edited for accuracy. This allows creators to easily create and upload captioned clips, allowing their audience to access their content more quickly and seamlessly.

As a result, these technologies are making it far more feasible for small content creators to expand their reach globally by facilitating subtitles in their native languages. This makes it easier to build a global fanbase without the large upfront investment that would otherwise be necessary to have an experienced translator transcribe content word-for-word.

However, ASR and machine translation still require assistance for the spotting and cutting process of turning transcripts into properly segmented, timed subtitles. This is known as an “expert in the loop” workflow, which ensures quality and efficiency. The goal is to get as close to a perfect match as possible between the spoken and written words, in order to keep viewers engaged with their content.

In addition, ASR and MT can help with a number of other translation processes, including QA, rephrasing, and editing. Ultimately, these tools are making the translation process more efficient for all involved. This will allow businesses to reduce costs, improve turnaround times, and ultimately deliver higher quality results.

However, while AI may be making the process of translation faster and more streamlined, it is not going to replace humans in this industry anytime soon. The most complex translations, including capturing tone and intent, require real flesh-and-bone writers to make sure that the right message is delivered every time.
5. Streamlined Workflows

When you integrate translation tools with other business systems, centralized data can help you optimize and automate workflows. This results in valuable time savings and boosts productivity across teams. For instance, using connectors for content management systems and translation services can eliminate the need for manual copy-and-paste tasks and makes it easier to track and review translations.

AI can also improve the efficiency of customer service and help businesses connect with global audiences like never before. By leveraging chatbots, AI can translate and deliver content to customers across languages and support agents in handling complicated inquiries with ease. This not only increases agent performance but ensures that customers are getting accurate answers and a personal touch.

Additionally, BPO providers are using AI translation solutions to enhance their services. For example, Concentrix Lingualab and Webhelp Polyglot have integrated translation technology with human linguists to provide multilingual contact centers. The result is a hybrid model that uses digital translators to handle simple, standardized customer inquiries while transferring more complex requests to language experts in specialized departments.

Another innovative use of translation AI is Dynamic Translation for Agent Chat (DTAC). This feature translates the entire conversation between requesters and agents. It helps them connect with more diverse customers while reducing the cost of hiring additional employees for hard-to-serve languages.

To maximize the benefits of AI, it is important to choose a platform that provides a range of language processing tools and features. It should also offer flexible pricing models to align with scalability requirements. Lastly, it is recommended to carry out pilot tests to identify any issues that might occur during the full-scale implementation of AI translation solutions.