Examples of artificial intelligence being used today in language services mostly feature machine translation. As an interdiscipline, machine translation is based on computational linguistics, AI and mathematical logic. Currently, AI language services are mostly applied to text translation, speech-to-speech translation and image translation.

Text translation is the most widely used form of machine translation. Speech-to-speech translation from a source language to a target language involves four steps: automatic speech recognition, algorithmic calculation, text conversion and speech conversion. The process can be seen as the AI version of simultaneous interpreting.

Similar to speech-to-speech translation, image translation also requires text recognition technology. It converts the image into text before the translated text is produced. Image translation is also gaining popularity. It can be used in many professional fields, such as medical treatment and autonomous vehicles.

Nevertheless, when it comes to quality, a gap still remains between machines and human translators. A report by the Translators Association of China revealed that 96.7% of the companies interviewed said they would choose machine translation only when the translation’s accuracy is guaranteed. It seems that AI translation still has a long way to go before it can meet the demands of the market. To better adjust the technology to the language service industry, we can start with four things:

First, broaden the service content. As science and technology advances with each passing day, the applied range of AI in the language service industry should be widened step by step. Instead of being restricted to machine translation, AI can also be applied to areas that are more professional, including technology research and development, research and consultation, assets management, marketing, training, investment, mergers and acquisitions. With the support of AI, the language service industry will be able to excavate and collect big data, which can be used to help us understand user demand and provide better language services and relevant services.

Second, revamp the service pattern. Outsourcing no longer suits the language service sector in the era of AI. Instead, the industry should become more service-oriented and product-oriented. While prioritizing customers’ demands, language service providers should be able to provide customized services with the help of new technologies and new platforms including AI, cloud computing, mobile internet, the internet of things and big data.

Third, innovate with service and technology. The industry should make full use of AI’s ability to calculate big data and simulate human brains’ thinking model, so that language project management tools and quality control tools can be developed to improve the quality and efficiency of language services.

Fourth, enhance language education. The application of AI in the language service sector has changed the training programs of language learners. Every year, about 10,000 students are enrolled in the Master of Interpreting and Translation in 281 colleges and universities that offer this major. As AI technology booms, colleges and universities have also included computer aided translation, localization and project management into their curriculum, so that language learners can better understand the new AI-supported translation methods.

Sun Maohua and Lu Min are from Nanjing Normal University.

(Edited and translated by Weng Rong)