Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks

Abstract

In cooperative dialogues, identifying the intent of ones conversation partner and acting accordingly is of great importance. While this endeavour is facilitated by phrasing intentions as directly as possible, we can observe in human-human communication that a number of factors such as cultural norms and politeness may result in expressing one's intent indirectly. Therefore, in human-computer communication we have to anticipate the possibility of users being indirect and be prepared to interpret their actual meaning. Furthermore, a dialogue system should be able to conform to human expectations by adjusting the degree of directness it uses to improve the user experience. To reach those goals, we propose an approach to differentiate between direct and indirect utterances and find utterances of the opposite characteristic that express the same intent. In this endeavour, we employ dialogue vector models and recurrent neural networks.

DIRECT: Direct and Indirect Responses in Conversational Text Corpus

“…Only a few recent studies have focused on pragmatic paraphrases to advance the understanding of users' intentions. Pragst and Ultes (2018) proposed a rule-based approach to automatically construct a corpus consisting of pairs of indirect and direct utterances. They demonstrated that the neural conversation model could accurately extract utterances with opposing directness.…”