Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2024

Abstract

Dialogue Aspect-based Sentiment Quadruple (DiaASQ) is a newly-emergent task aiming to extract the sentiment quadruple (i.e., targets, aspects, opinions, and sentiments) from conversations. While showing promising performance, the prior DiaASQ approach unfortunately falls prey to the key crux of DiaASQ, including insufficient modeling of discourse features, and lacking quadruple extraction, which hinders furthertask improvement. To this end, we introduce a novel framework that not only capitalizes on comprehensive discourse feature modeling, but also captures the intrinsic interaction for optimal quadruple extraction. On the one hand, drawing upon multiple discourse features, our approach constructs a token-level heterogeneous graph and enhances token interactions through a heterogeneous attention network. We further propose a novel triadic scorer, strengthening weak token relations within a quadruple, thereby enhancing the cohesion of the quadruple extraction. Experimental results on the DiaASQ benchmark showcase that our model significantly out-performs existing baselines across both English and Chinesedatasets. Our code is available at https://bit.ly/3v27pqA.

Keywords

Feature models, Heterogeneous graph, Performance

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of the The 38th Annual AAAI Conference on Artificial Intelligence, Vancouver, Canada, 2024 February 20-27

Volume

38

First Page

18462

Last Page

18470

Identifier

10.1609/aaai.v38i16.29807

Publisher

AAAI Press

City or Country

Washington, DC

Additional URL

https://doi.org/10.1609/aaai.v38i16.29807

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