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

Louisa Pragst, Stefan Ultes


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.
Anthology ID:
W18-5002
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
SIGDIAL | WS
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–19
Language:
URL:
https://aclanthology.org/W18-5002
DOI:
10.18653/v1/W18-5002
Bibkey:
Cite (ACL):
Louisa Pragst and Stefan Ultes. 2018. Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 11–19, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks (Pragst & Ultes, 2018)
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PDF:
https://aclanthology.org/W18-5002.pdf