Our Analysts' Insights

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:

1. Most intelligent content frameworks support only a handful of languages. The most common frameworks are essentially monolingual and built around English or Chinese, with some support for French, Spanish, German, or other major languages. Although it might seem trivial to localize strings in these development frameworks, this lack of language support is a problem.
    
2. Speech technology still lags for voice-enabled applications. Although it is getting better, automatic speech recognition (ASR) still struggles with accents and nonstandard dialects, slang, unclear speech, and out-of-vocabulary items. Even a few misunderstandings can change the course of an interaction. Privacy concerns can make it hard to bring humans into the loop to improve outcomes.
    
3. Lack of state in applications frustrates users. Data privacy concerns prevent developers from storing information on the history of user interactions with services. For example, if a phone owner asks Siri or a similar service for directions to Germersheim in Germany, it can launch a mapping application. However, if five minutes later the user asks, “What are the top tourist attractions there?” these applications will not know what “there” means because their developers have no way of tracking what happened in the conversation moments before.

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.
 

Steps to Improve Chances of Success

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:

1. Do more than translate strings. Rather than creating translated versions of resource files and plugging them back into the source agent, treat each localized version as an independent product. Although you may be able to borrow some translated assets, be prepared to engineer language-specific solutions. The results may be more like transcreation than traditional localization, but they are more likely to meet expectations.
    
2. Develop around a common set of capabilities. Different frameworks for creating conversational agents have different feature sets. Many developers report difficulty when they build chatbots or other agents on a platform that supports certain functionality only to find that it is not available on common platforms in other countries. To address this difficulty, list the platforms and frameworks you will need to support for each market. These will vary from fully speech-enabled ones in some markets to simple SMS-based text agents in others. Based on what you want the agent to accomplish, find a path to success on a common set of features. If this is not possible, do as much as you can and document the areas where you will need separate engineering effort for some markets.
    
3. Be willing to scale back plans. Management may want an intelligent conversational agent that can do everything, but such efforts will almost certainly fail when multiple markets are factored in. You are much more likely to achieve success with smaller, focused agents. This approach will usually deliver better results in your source language as well. If you need an agent that can address many needs, create a selection mechanism up front that routes customers to a purpose-built agent that meets their needs. In addition, even if you use a machine learning-based framework in your home market, you may find that a “rails-driven” agent will provide better outcomes in your target markets because it does not depend on the ability to understand unconstrained speech. This type of chatbot instead relies on simple questions with answers such as “Press or Say ‘Yes’ or ‘No’” to guide users down pre-defined paths “rails” to meet their needs.

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.