Natural Language Processing Strives to Meet Star Trek Standard
These deployments, though, are few and far between. Most contact centers still are not using natural language processing.
Last year, Software Advice, a Gartner company that assists organizations in finding the products that best fit their needs, called the IVR systems of 50 Fortune 500 companies with business models that focus on customer service. Only two prompted the caller for an open-ended natural language response instead of offering a menu.
Tetschner notes that across vertical market segments, speech is included in only 23.1 percent of automated call steering applications. The vast majority of those with a speech interface use directed dialogue, not natural language.
Out of 1,202 corporate auto-attendants tested by Tetschner, fewer than 100 (8 percent) had a speech interface. Of those with speech capabilities, only four had natural language interfaces.
"When it comes to true natural language, there just aren’t many implementations out there," Tetschner says.
"Many call centers have been slow to adopt the technology beyond trials," says Bill Meisel, president of TMA Associates and executive director of the Applied Voice Input/Output Society (AVIOS).
In reality, the majority of speech applications in use today are of the directed-dialogue variety. When combined with well-designed prompts and call flows, the higher accuracy of directed dialogue applications can offer a superior user experience, many industry experts conclude.
Callers can surprise any speech-enabled system by saying things that aren't on its preprogrammed list of expected responses. Natural language technologies, though, can be more forgiving and understand more caller responses, but they come at a price. Developers of such systems need to be able to predict everything callers might say and build grammars in advance that contain each word and phrase to be recognized.
This differs greatly from directed-dialogue systems. Because callers are being directed to say very specific options, there are fewer likely responses to account for in the grammar. This makes directed-dialogue systems easier to develop, and they require less time to troubleshoot and test.
But here, too, technology has solved many of those issues. "The fear was that [with natural language] you would have to build huge dictionaries," Kaplan says. "Technology has largely overcome this. It's not so much of a fear-inducing thing where you have to worry that it will be so much work."
Most businesses will only have to take the words specific to their industries and incorporate them into the dictionary, he says. Kaplan, nonetheless, recommends that companies looking to deploy a natural language interface "be clear about the types of things you want people to be able to do. Know what services you want to provide and how people might ask for it," he says.
There are many other reasons for not going with natural language systems. Cost is perhaps the greatest. According to Meisel, natural language systems can be expensive, especially if changes need to be made. Changes, he says, usually require more data gathering, testing, and the involvement of vendor professional support.
"The prices are enormous," Tetschner says, noting that a natural language interface can cost $500,000 to implement because of all the customization and data that is required.
But, he says, the added costs can be offset over time by increases in customer self-service. Additionally, "a lot of the hang-ups will disappear because customers will stay on the call," Tetschner points out.
Research firm MarketsandMarkets Expects the natural language processing market to reach $16 billion in five years.