Abstract
Multifaceted ideology detection (MID) aims to detect the ideological leanings of texts towards multiple facets. Previous studies on ideology detection mainly focus on one generic facet and ignore label semantics and explanatory descriptions of ideologies, which are a kind of instructive information and reveal the specific concepts of ideologies. In this paper, we develop a novel concept semantics-enhanced framework for the MID task. Specifically, we propose a bidirectional iterative concept flow (BICo) method to encode multifaceted ideologies. BICo enables the concepts to flow across levels of the schema tree and enriches concept representations with multi-granularity semantics. Furthermore, we explore concept attentive matching and concept-guided contrastive learning strategies to guide the model to capture ideology features with the learned concept semantics. Extensive experiments on the benchmark dataset show that our approach achieves state-of-the-art performance in MID, including in the cross-topic scenario.- Anthology ID:
- 2024.findings-acl.172
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2930–2942
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.172
- DOI:
- Cite (ACL):
- Songtao Liu, Bang Wang, Wei Xiang, Han Xu, and Minghua Xu. 2024. Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection. In Findings of the Association for Computational Linguistics ACL 2024, pages 2930–2942, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection (Liu et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.172.pdf