Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework

Ruike Zhang, Hanxuan Yang, Wenji Mao


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
Bibkey:
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)
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PDF:
https://aclanthology.org/2023.emnlp-main.666.pdf
Video:
 https://aclanthology.org/2023.emnlp-main.666.mp4