Interaction Quality Estimation Using Long Short-Term Memories

Niklas Rach, Wolfgang Minker, Stefan Ultes


Abstract
For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS), the dialogue history is of significant importance. Previous works included this information manually in the form of precomputed temporal features into the classification process. Here, we employ a deep learning architecture based on Long Short-Term Memories (LSTM) to extract this information automatically from the data, thus estimating IQ solely by using current exchange features. We show that it is thereby possible to achieve competitive results as in a scenario where manually optimized temporal features have been included.
Anthology ID:
W17-5520
Volume:
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
Month:
August
Year:
2017
Address:
Saarbrücken, Germany
Venues:
SIGDIAL | WS
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
164–169
Language:
URL:
https://aclanthology.org/W17-5520
DOI:
10.18653/v1/W17-5520
Bibkey:
Cite (ACL):
Niklas Rach, Wolfgang Minker, and Stefan Ultes. 2017. Interaction Quality Estimation Using Long Short-Term Memories. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 164–169, Saarbrücken, Germany. Association for Computational Linguistics.
Cite (Informal):
Interaction Quality Estimation Using Long Short-Term Memories (Rach et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5520.pdf