A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging
Year: 2022
Type: other
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 taxonomy 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, on a subset of the ISO 24617-2 taxonomy. 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 bench- mark data set, providing empirical evidence that this architecture can successfully generalise to different domains.
Source: Underline Science Inc.
DOI: 10.48448/73ph-x489
Authors: International Committee for Computational Linguistics 2022, Blair, Alan, Mezza, Stefano, Wobcke, Wayne
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6929219663
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