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.- Anthology ID:
- 2024.lrec-main.1355
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 15585–15594
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1355
- DOI:
- Cite (ACL):
- Shaokang Wang and Li Pan. 2024. Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15585–15594, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection (Wang & Pan, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1355.pdf