Abstract
Stance detection aims to identify the user’s attitude toward specific targets from text, which is an important research area in text mining and benefits a variety of application domains. Existing studies on stance detection were conducted mainly in English. Due to the low-resource problem in most non-English languages, cross-lingual stance detection was proposed to transfer knowledge from high-resource (source) language to low-resource (target) language. However, previous research has ignored the practical issue of no labeled training data available in target language. Moreover, target inconsistency in cross-lingual stance detection brings about the additional issue of unseen targets in target language, which in essence requires the transfer of both language and target-oriented knowledge from source to target language. To tackle these challenging issues, in this paper, we propose the new task of cross-lingual cross-target stance detection and develop the first computational work with dual knowledge distillation. Our proposed framework designs a cross-lingual teacher and a cross-target teacher using the source language data and a dual distillation process that transfers the two types of knowledge to target language. To bridge the target discrepancy between languages, cross-target teacher mines target category information and generalizes it to the unseen targets in target language via category-oriented learning. Experimental results on multilingual stance datasets demonstrate the effectiveness of our method compared to the competitive baselines.- Anthology ID:
- 2023.emnlp-main.666
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10804–10819
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.666
- DOI:
- 10.18653/v1/2023.emnlp-main.666
- Cite (ACL):
- Ruike Zhang, Hanxuan Yang, and Wenji Mao. 2023. Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10804–10819, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework (Zhang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-main.666.pdf