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Chatbots, machine-authored text, and automated information retrieval and summarization are increasingly important to global businesses seeking to interact more efficiently with customers and meet their needs. Recent developments in this area have been spectacular and AI-driven intelligent agents (chatbots and virtual digital assistants, such as Siri, Cortana, and Alexa) are among the most visible success cases for machine-generated content. Enterprises look to these applications as ways to reduce costs, engage users, and creates entirely new classes of goods and services. Unfortunately, as they seek to build these services, they very quickly run into walls that threaten the viability of their development efforts:
However, an even bigger problem from a localization perspective is that conversations do not follow the same rules and expectations across cultures and languages. For example, a chatbot developed in the U.S. might ask, “What’s your name?” early in a discussion, store it in a session variable, and use it frequently in dialogue such as, “Thank you, William. When do you want to book that flight?” Individuals interacting with a chatbot in other markets might find use of a given name in this fashion to be too informal or “American.” Although it may be simple to re-engineer a conversational agent to avoid using first names, other problems are not as easy to resolve. Conversations flow differently in different languages and countries: When to ask for specific information – or even what to ask for – may not be the same. Some translation vendors promote the idea that enterprises leverage machine translation as an “intercept layer” on top of a chatbot in order to avoid the expense and difficulty of translating it. However, even in cases where differences may not be immediately apparent, factors such as indirect ways of asking or answering questions can throw agents into confusion. For example, if a chatbot is built on U.S. English training data and asks a yes-or-no question to which a British user responds, “That would be lovely,” this typical English response may cause the agent to stop, even without the need for translation. Add in the factor of machine translation from German to English or French to Chinese and small differences can quickly add up.
Not every project will succeed. Work with conversational agents is a new and exciting field, but developers still need to figure out how to make them work around the world. Your goal is to improve the likelihood that you will deliver conversational agents successfully in various markets. If you are localizing them, you can boost your chances by doing the following:
Although localizing conversational agents is a challenge, increasing numbers of enterprises are finding success in this area. Increasing awareness of international issues and how they affect these applications is also helping drive better support in frameworks, and these projects will become easier over time. In a follow-up piece, we will turn to the business and conceptual difficulties that enterprises face when building chatbots and how to solve them.
Senior Analyst
Focuses on language technology, artificial intelligence, translation quality, and overall economic factors impacting globalization
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