@inproceedings{jiang-etal-2024-relation-classification,
title = "Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model",
author = "Jiang, Yizhi and
Li, Jinlong and
Chen, Huanhuan",
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.1212",
pages = "13885--13897",
abstract = "The Relation Extraction (RE) task aims to extract the relation between two entities in a sentence. As the performance of methods on RE task depends on datasets{'} quantity and quality, in this paper, we propose to use the Large Language Model (LLM) to do data augmentation. Moreover, compared to traditional fine-tuning methods, more research focuses on prompt learning. However, all of their prompt templates ignore the relative order of entities, which we believe will affect the prediction error. Due to that, we propose novel bidirectional prompt templates for prompt learning and design a training strategy for utilizing the templates. Then we try to fit the probability distributions of both prompt learning and fine-tuning methods into our model. To this end, we propose Relation Classification via Bidirectional Prompt learning with data augmentation by LLM (RCBP) and conduct experiments on four datasets: TACRED, RETACRED, TACREV and Semeval. The results show that RCBP performs well on these datasets and outperforms the state-of-the-art in the TACREV, RETACRED datasets.",
}
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<abstract>The Relation Extraction (RE) task aims to extract the relation between two entities in a sentence. As the performance of methods on RE task depends on datasets’ quantity and quality, in this paper, we propose to use the Large Language Model (LLM) to do data augmentation. Moreover, compared to traditional fine-tuning methods, more research focuses on prompt learning. However, all of their prompt templates ignore the relative order of entities, which we believe will affect the prediction error. Due to that, we propose novel bidirectional prompt templates for prompt learning and design a training strategy for utilizing the templates. Then we try to fit the probability distributions of both prompt learning and fine-tuning methods into our model. To this end, we propose Relation Classification via Bidirectional Prompt learning with data augmentation by LLM (RCBP) and conduct experiments on four datasets: TACRED, RETACRED, TACREV and Semeval. The results show that RCBP performs well on these datasets and outperforms the state-of-the-art in the TACREV, RETACRED datasets.</abstract>
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%0 Conference Proceedings
%T Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model
%A Jiang, Yizhi
%A Li, Jinlong
%A Chen, Huanhuan
%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 jiang-etal-2024-relation-classification
%X The Relation Extraction (RE) task aims to extract the relation between two entities in a sentence. As the performance of methods on RE task depends on datasets’ quantity and quality, in this paper, we propose to use the Large Language Model (LLM) to do data augmentation. Moreover, compared to traditional fine-tuning methods, more research focuses on prompt learning. However, all of their prompt templates ignore the relative order of entities, which we believe will affect the prediction error. Due to that, we propose novel bidirectional prompt templates for prompt learning and design a training strategy for utilizing the templates. Then we try to fit the probability distributions of both prompt learning and fine-tuning methods into our model. To this end, we propose Relation Classification via Bidirectional Prompt learning with data augmentation by LLM (RCBP) and conduct experiments on four datasets: TACRED, RETACRED, TACREV and Semeval. The results show that RCBP performs well on these datasets and outperforms the state-of-the-art in the TACREV, RETACRED datasets.
%U https://aclanthology.org/2024.lrec-main.1212
%P 13885-13897
Markdown (Informal)
[Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model](https://aclanthology.org/2024.lrec-main.1212) (Jiang et al., LREC-COLING 2024)
ACL