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:
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.coling-1.45.pdf
- Data
- DailyDialog