@inproceedings{an-etal-2024-knowledge,
    title = "Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic",
    author = "An, Hao  and
      Zhu, Zhihong  and
      Cheng, Xuxin  and
      Huang, Zhiqi  and
      Zou, Yuexian",
    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.858",
    pages = "9822--9831",
    abstract = "Dialogue-based relation extraction (DRE) aims to determine the semantic relation of a given pair of arguments from a piece of dialogue, which has received increasing attention. Due to the low information density of dialogue text, it is difficult for the model to focus on key information. To this end, in this paper, we propose a Knowledge-Enhanced Prompt-Tuning (KEPT) method to effectively enhance DRE model by exploiting trigger and label semantic. Specifically, we propose two beneficial tasks, masked trigger prediction, and verbalizer representation learning, to effectively inject trigger knowledge and label semantic knowledge respectively. Furthermore, we convert the DRE task to a masked language modeling task to unify the format of knowledge injection and utilization, aiming to better promote DRE performance. Experimental results on the DialogRE dataset show that our KEPT achieves state-of-the-art performance in F1 and F1c scores. Detailed analyses demonstrate the effectiveness and efficiency of our proposed approach. Code is available at https://github.com/blackbookay/KEPT.",
}

Dialogue-based relation extraction (DRE) aims to determine the semantic relation of a given pair of arguments from a piece of dialogue, which has received increasing attention. Due to the low information density of dialogue text, it is difficult for the model to focus on key information. To this end, in this paper, we propose a Knowledge-Enhanced Prompt-Tuning (KEPT) method to effectively enhance DRE model by exploiting trigger and label semantic. Specifically, we propose two beneficial tasks, masked trigger prediction, and verbalizer representation learning, to effectively inject trigger knowledge and label semantic knowledge respectively. Furthermore, we convert the DRE task to a masked language modeling task to unify the format of knowledge injection and utilization, aiming to better promote DRE performance. Experimental results on the DialogRE dataset show that our KEPT achieves state-of-the-art performance in F1 and F1c scores. Detailed analyses demonstrate the effectiveness and efficiency of our proposed approach. Code is available at https://github.com/blackbookay/KEPT.
An, Hao Zhu, Zhihong Cheng, Xuxin Huang, Zhiqi Zou, Yuexian
Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic