Direct Answer Threshold Optimization in Dialogue Systems

Abstract

The presence of dialogue systems is rising in a wide array of industries. While complex, human-like conversational flow and turn-taking have been the focus of recent research advances, we make the point that direct answers are equally important in human-bot interactions. This is especially true in an information seeking task where prompt, correct answers with minimal back-and-forth are desirable. We define a direct answer as a response given to a user query without requiring further clarifications from the user. To determine whether a direct answer is to be given or not, a threshold is applied to the to the confidence level of the predicted intent; in the case where the confidence is higher than the threshold, the user receives a direct answer. This threshold is often set intuitively or on the basis of a few observations, usually between 50% - 75%. In this paper, we propose a method to estimate this threshold based on the intent classification confidence level combined with several intent volumetrics. The goal of our method is to maximize the number of correct direct responses for as many intents as possible in order to minimize user frustration from unnecessary requests for clarifications. Moreover, our method is applicable in the earlier stages of a dialogue system when real interaction logs are scarce. We show that our method improves the accuracy of directly answered queries by 3 to 14% while maximizing the number of accurately answered intents on two dialogue system datasets of 32 and 152 intents.