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        <title>Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework</title>
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        <namePart type="given">Ruike</namePart>
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        <namePart type="given">Hanxuan</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.666</identifier>
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%0 Conference Proceedings
%T Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework
%A Zhang, Ruike
%A Yang, Hanxuan
%A Mao, Wenji
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-cross-lingual
%X 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.
%R 10.18653/v1/2023.emnlp-main.666
%U https://aclanthology.org/2023.emnlp-main.666
%U https://doi.org/10.18653/v1/2023.emnlp-main.666
%P 10804-10819

Markdown (Informal)

[Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework](https://aclanthology.org/2023.emnlp-main.666) (Zhang et al., EMNLP 2023)

• Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework (Zhang et al., EMNLP 2023)

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.