A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging

Stefano Mezza, Wayne Wobcke, Alan Blair


Abstract
Dialogue Act tagging with the ISO 24617-2 standard is a difficult task that involves multi-label text classification across a diverse set of labels covering semantic, syntactic and pragmatic aspects of dialogue. The lack of an adequately sized training set annotated with this standard is a major problem when using the standard in practice. In this work we propose a neural architecture to increase classification accuracy, especially on low-frequency fine-grained tags. Our model takes advantage of the hierarchical structure of the ISO taxonomy and utilises syntactic information in the form of Part-Of-Speech and dependency tags, in addition to contextual information from previous turns. We train our architecture on an aggregated corpus of conversations from different domains, which provides a variety of dialogue interactions and linguistic registers. Our approach achieves state-of-the-art tagging results on the DialogBank benchmark data set, providing empirical evidence that this architecture can successfully generalise to different domains.
Anthology ID:
2022.coling-1.45
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
542–552
Language:
URL:
https://aclanthology.org/2022.coling-1.45
DOI:
Bibkey:
Cite (ACL):
Stefano Mezza, Wayne Wobcke, and Alan Blair. 2022. A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging. In Proceedings of the 29th International Conference on Computational Linguistics, pages 542–552, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging (Mezza et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.45.pdf
Data
DailyDialog