@inproceedings{wang-pan-2024-target-adaptive,
title = "Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection",
author = "Wang, Shangkang and
Pan, Li",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1355",
pages = "15585--15594",
abstract = "Stance detection is a fundamental task in Natural Language Processing (NLP). It is challenging due to diverse expressions and topics related to the targets from multiple domains. Recently, prompt-tuning has been introduced to convert the original task into a cloze-style prediction task, achieving impressive results. Many prompt-tuning-based methods focus on one or two classic scenarios with concrete external knowledge enhancement. However, when facing intricate information in multi-domain stance detection, these methods cannot be adaptive to multi-domain semantics. In this paper, we propose a novel target-adaptive consistency enhanced prompt-tuning method (TCP) for stance detection with multiple domains. TCP incorporates target knowledge and prior knowledge to construct target-adaptive verbalizers for diverse domains and employs pilot experiments distillation to enhance the consistency between verbalizers and model training. Specifically, to capture the knowledge from multiple domains, TCP uses a target-adaptive candidate mining strategy to obtain the domain-related candidates. Then, TCP refines them with prior attributes to ensure prediction consistency. The Pre-trained Language Models (PLMs) in prompt-tuning are with large-scale parameters, while only changing the verbalizer without corresponding tuning has a limited impact on the training process. Target-aware pilot experiments are conducted to enhance the consistency between the verbalizer and training by distilling the target-adaptive knowledge into prompt-tuning. Extensive experiments and ablation studies demonstrate that TCP outperforms the state-of-the-art methods on nine stance detection datasets from multiple domains.",
}
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<abstract>Stance detection is a fundamental task in Natural Language Processing (NLP). It is challenging due to diverse expressions and topics related to the targets from multiple domains. Recently, prompt-tuning has been introduced to convert the original task into a cloze-style prediction task, achieving impressive results. Many prompt-tuning-based methods focus on one or two classic scenarios with concrete external knowledge enhancement. However, when facing intricate information in multi-domain stance detection, these methods cannot be adaptive to multi-domain semantics. In this paper, we propose a novel target-adaptive consistency enhanced prompt-tuning method (TCP) for stance detection with multiple domains. TCP incorporates target knowledge and prior knowledge to construct target-adaptive verbalizers for diverse domains and employs pilot experiments distillation to enhance the consistency between verbalizers and model training. Specifically, to capture the knowledge from multiple domains, TCP uses a target-adaptive candidate mining strategy to obtain the domain-related candidates. Then, TCP refines them with prior attributes to ensure prediction consistency. The Pre-trained Language Models (PLMs) in prompt-tuning are with large-scale parameters, while only changing the verbalizer without corresponding tuning has a limited impact on the training process. Target-aware pilot experiments are conducted to enhance the consistency between the verbalizer and training by distilling the target-adaptive knowledge into prompt-tuning. Extensive experiments and ablation studies demonstrate that TCP outperforms the state-of-the-art methods on nine stance detection datasets from multiple domains.</abstract>
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%0 Conference Proceedings
%T Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection
%A Wang, Shangkang
%A Pan, Li
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F wang-pan-2024-target-adaptive
%X Stance detection is a fundamental task in Natural Language Processing (NLP). It is challenging due to diverse expressions and topics related to the targets from multiple domains. Recently, prompt-tuning has been introduced to convert the original task into a cloze-style prediction task, achieving impressive results. Many prompt-tuning-based methods focus on one or two classic scenarios with concrete external knowledge enhancement. However, when facing intricate information in multi-domain stance detection, these methods cannot be adaptive to multi-domain semantics. In this paper, we propose a novel target-adaptive consistency enhanced prompt-tuning method (TCP) for stance detection with multiple domains. TCP incorporates target knowledge and prior knowledge to construct target-adaptive verbalizers for diverse domains and employs pilot experiments distillation to enhance the consistency between verbalizers and model training. Specifically, to capture the knowledge from multiple domains, TCP uses a target-adaptive candidate mining strategy to obtain the domain-related candidates. Then, TCP refines them with prior attributes to ensure prediction consistency. The Pre-trained Language Models (PLMs) in prompt-tuning are with large-scale parameters, while only changing the verbalizer without corresponding tuning has a limited impact on the training process. Target-aware pilot experiments are conducted to enhance the consistency between the verbalizer and training by distilling the target-adaptive knowledge into prompt-tuning. Extensive experiments and ablation studies demonstrate that TCP outperforms the state-of-the-art methods on nine stance detection datasets from multiple domains.
%U https://aclanthology.org/2024.lrec-main.1355
%P 15585-15594
Markdown (Informal)
[Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection](https://aclanthology.org/2024.lrec-main.1355) (Wang & Pan, LREC-COLING 2024)
ACL