Adaptable Conversational Machines

Abstract

In recent years we have witnessed a surge in machine learning methods that provide machines with conversational abilities. Most notably, neural-network–based systems have set the state of the art for difficult tasks such as speech recognition, semantic understanding, dialogue management, language generation, and speech synthesis. Still, unlike for the ancient game of Go for instance, we are far from achieving human-level performance in dialogue. The reasons for this are numerous. One property of human–human dialogue that stands out is the infinite number of possibilities of expressing oneself during the conversation, even when the topic of the conversation is restricted. A typical solution to this problem was scaling-up the data. The most prominent mantra in speech and language technology has been “There is no data like more data.” However, the researchers now are focused on building smarter algorithms — algorithms that can learn efficiently from just a few examples. This is an intrinsic property of human behavior: an average human sees during their lifetime a fraction of data that we nowadays present to machines. A human can even have an intuition about a solution before ever experiencing an example solution. The human-inspired ability to adapt may just be one of the keys in pushing dialogue systems toward human performance. This article reviews advancements in dialogue systems research with a focus on the adaptation methods for dialogue modeling, and ventures to have a glance at the future of research on adaptable conversational machines.