Unbabel, the AI-powered, human-refined translation platform that enables multilingual customer service at scale, announced that the company’s AI research team is partnering with Carnegie Mellon University, INESC-ID, and Instituto de Telecomunicações to dramatically improve multilingual conversational chat. The MAIA: Multilingual AI Agent Assistants large scale research project will augment customer service agents with AI, making it more efficient for enterprises to deliver chat in 30 languages and improve customer satisfaction through human empathy.
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Unbabel announces MAIA research project to dramatically improve multilingual conversational chat.
“While chat is becoming one of the preferred means for customer service, it currently faces important limitations that revolve around supporting customers in their native language to drive better customer satisfaction, while maintaining the human-to-human experience,” said Andre Martins, vice president of AI research at Unbabel. “The MAIA research project will enable customer service and support professionals to capture context and establish empathy with customers by infusing a machine and human approach, making the process more seamless and accurate.”
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Over the course of 36 months, the research team proposes to build a toolbox of machine learning technologies for online multilingual customer service, which includes context-aware machine translation, automatic answer generation, and conversational quality estimation. This will significantly expand the current portfolio of Unbabel AI technologies, creating a new model: an agent assistant that will facilitate the communication between human agents and international customers, making live chat customer service platforms more multilingual, scalable and capable of ensuring higher customer satisfaction. It will also help agents with new dialogue-oriented productivity tools, and expand Unbabel’s renowned quality estimation technology to assess conversational quality.
“As the global economy becomes even more connected, it is more important than ever to be able to communicate effectively across cultural borders,” said Graham Neubig, associate professor at Carnegie Mellon University. “Tackling this task through machine translation is challenging due to the need to consider relevant conversational context in order to generate precise, polite, and culturally appropriate translations. At the same time, the interactive nature of conversations poses a number of new opportunities for translation technology. The MAIA project will advance natural language processing (NLP) and machine learning technology to tackle these challenges and opportunities, increasing the speed, quality, and user experience of multilingual machine-mediated conversations.”
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