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Data Scientists: Use Sophisticated Readability Strategies When Developing AI Chatbots

9 January, 2017

Data Scientists: Use Sophisticated Readability Strategies When Developing AI Chatbots

When working with AI chatbots and Natural Language Generation in data science solutions, don’t try to outsmart customers. Your goal is to design a system that talks directly at their level.

Have you recently had a frustrating or confusing conversation with a computer?

It’s silly to admit, but once in while I find myself screaming at Alexa (i.e., the persona of my Amazon Echo) because I don’t like what she’s telling me. Rationally, I know it’s not her fault that my vacation plans are probably ruined by impending thunderstorms, but it’s easy to get caught up in the role-play when your computer is talking to you like a human. For artificial intelligence architects, this is known as Natural Language Generation or NLG.

A bigger challenge for solution developers embracing NLG in their designs is communicating in the best way for their customers to appreciate and understand. With the recent advances in NLG technology and subsequent applied technology like Alexa, customers are expecting more than just any response—they’re expecting an intelligently articulated response that’s both comprehensible and stimulating.

I feel solution designers are missing the mark here. If your next product or service involves having a conversation with customers, make sure your system is talking at their level—not above or below it.

Competitive strategy: Start simple

Readability is the industry term that’s commonly used to gauge whether your text is appropriate for your audience, though I prefer the term understandability. Most people can read and even pronounce the word gallimaufry, but not many people understand what it means without context (it means a jumble or hodgepodge). Your real goal in NLG is ease of comprehension, but we’ll stick with readability for now since that’s the lexicon that’s been adopted.

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