Neural-based Context Representation Learning for Dialog Act Classification

Daniel Ortega, Ngoc Thang Vu


Abstract
We explore context representation learning methods in neural-based models for dialog act classification. We propose and compare extensively different methods which combine recurrent neural network architectures and attention mechanisms (AMs) at different context levels. Our experimental results on two benchmark datasets show consistent improvements compared to the models without contextual information and reveal that the most suitable AM in the architecture depends on the nature of the dataset.
Anthology ID:
W17-5530
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:
247–252
Language:
URL:
https://aclanthology.org/W17-5530
DOI:
10.18653/v1/W17-5530
Bibkey:
Cite (ACL):
Daniel Ortega and Ngoc Thang Vu. 2017. Neural-based Context Representation Learning for Dialog Act Classification. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 247–252, Saarbrücken, Germany. Association for Computational Linguistics.
Cite (Informal):
Neural-based Context Representation Learning for Dialog Act Classification (Ortega & Vu, 2017)
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PDF:
https://aclanthology.org/W17-5530.pdf