Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation

Xuanli He, Quan Tran, William Havard, Laurent Besacier, Ingrid Zukerman, Gholamreza Haffari


Abstract
In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR)’s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results.
Anthology ID:
U18-1007
Volume:
Proceedings of the Australasian Language Technology Association Workshop 2018
Month:
December
Year:
2018
Address:
Dunedin, New Zealand
Venue:
ALTA
SIG:
Publisher:
Note:
Pages:
61–65
Language:
URL:
https://aclanthology.org/U18-1007
DOI:
Bibkey:
Cite (ACL):
Xuanli He, Quan Tran, William Havard, Laurent Besacier, Ingrid Zukerman, and Gholamreza Haffari. 2018. Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation. In Proceedings of the Australasian Language Technology Association Workshop 2018, pages 61–65, Dunedin, New Zealand.
Cite (Informal):
Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation (He et al., ALTA 2018)
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PDF:
https://aclanthology.org/U18-1007.pdf