We Need Answers: Conversational Systems and Answer Technology

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At AVIOS’s Bots & Assistants Conference in November, I led a panel discussion that got at a major issue with conversational systems: Though the accuracy of speech recognition and natural language understanding will continue to advance rapidly (thanks, in part, to the increasing amount of data available to drive machine learning), “answer technology”—which governs the ability of digital assistants to answer user questions—remains a big challenge.

The panelists—Jeff Blankenburg, principal technical evangelist for Amazon Alexa, and David Nahamoo, chief technology officer at Pryon—noted that breakthroughs with conversational technology are happening all the time, including the ability to personalize digital assistants so that they retain information about specific users and past conversations (though this could raise privacy concerns, like with healthcare data).

But both cautioned that there’s a bottleneck when it comes to providing automated answers to user questions. Answer technology is the part of a conversational system that connects to knowledge sources to address the intent of users. Today, digital assistants with a visual interface default to a web search when they don’t have a direct answer, providing a list of websites that might contain it. The goal of answer technology should be to provide a direct answer or perhaps ask a clarifying question that enables it to provide a direct answer.

Companies are motivated to provide customers with automated answers to reduce costs and provide faster customer service. Nahamoo also noted that employees are often overwhelmed by the amount of knowledge they need to do their jobs and need a quick way to discover thatknowledge.

Today, the major type of user request that can yield direct answers are “Frequently Asked Questions” (FAQs). These, Blankenburg indicated, are often addressed by decision trees generated by humans, with the trees driven by keywords in the request. Decision trees can also be generated by machine learning rather than humans, but this requires a large database of labeled data with requests for specific answers phrased in many different ways, in practice largely limiting the approach to FAQs.

And the panelists noted a further difficulty: Much of the source data that contains answers is unstructured text, often distributed across websites, reports, books, magazines, newsletters, and other highly variable formats. Simply searching such documents for keywords doesn’t sufficiently distinguish appropriate answers.

As conversational technology grows as a popular alternative to manual web searches, it will motivate the assembling of source data in a form that makes it easier to find answers. One approach that we sometimes see that can at least narrow the documents searched is a list of keywords and phrases that identify the main context of the article.

Another approach that allows drilling down further in a document determined to be relevant is to use informative headings and subheadings for sections of unstructured documents, headings that, in effect, act as labels for potential answers. An automated system could search headings first with the knowledge that these are more than simply words in the document, but indicators of major content.

Such headings would have to be identified in the text as such. Microsoft Word has this kind of labeling as an option with its “Style” feature, where you can choose a format for headings and subheadings. These labels are searchable, as evidenced by Word’s ability to use them to create a table of contents or an outline.

A similar feature in other text formats, including Acrobat PDF files, could make this a more universal option. Perhaps a standard method of denoting headings could be formalized.

Answer technology could be the missing link to making conversational technology a universal approach to delivering knowledge. But to be fully effective, it might require a cooperative effort between those developing the digital assistant technology and those providing the sources of answers. x

William Meisel is president of TMA Associates (www.tmaa.com) and executive director of AVIOS (www.avios.org).

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