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Cited by 41Speech and dialogue systemsTopic ModelingSpeech Recognition and Synthesis

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Neural-based Context Representation Learning for Dialog Act Classification

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Ortega, Daniel, Vu, Ngoc Thang·2017·Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
DOI 10.18653/v1/w17-5530
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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.

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Figure 1 : Figure 1: Model architecture for DA classification. On the left side is the overview of the model. The right site contains six neural-based methods for context representation learning.
Figure 1 : Figure 1: Model architecture for DA classification. On the left side is the overview of the model. The right site contains six neural-based methods for context representation learning.CC-BY · From the PDF

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Metadata

Authors
Ortega, D., & Vu, N. T.
Affiliations
University of Stuttgart (DE)
ORCID
Vu, Ngoc Thang
Venue
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
Publisher
Association for Computational Linguistics
Published
2017
Pages
247-252
DOI
10.18653/v1/w17-5530
OpenAlex
W2744378813
Cited by
41
References
2
Citations by year
2026: 1 · 2025: 2 · 2024: 2 · 2023: 8 · 2022: 4 · 2021: 6 · 2020: 4 · 2019: 5 · 2018: 9
FWCI
1.98
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Annual Meeting of the Special Interest Group on Discourse and Dialogue (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
dialogue act classification, attention mechanism, recurrent neural networks, contextual information, dialogue understanding

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