We present FinTree, Financial Dataset Pretrain Transformer Encoder for
Relation Extraction. Utilizing an encoder language model, we further pretrain
FinTree on the financial dataset, adapting the model in financial domain tasks.
FinTree stands out with its novel structure that predicts a masked token
instead of the conventional [CLS] token, inspired by the Pattern Exploiting
Training methodology. This structure allows for more accurate relation
predictions between two given entities. The model is trained with a unique
input pattern to provide contextual and positional information about the
entities of interest, and a post-processing step ensures accurate predictions
in line with the entity types. Our experiments demonstrate that FinTree
outperforms on the REFinD, a large-scale financial relation extraction dataset.
The code and pretrained models are available at
this https URL

金融数据集预训练转换编码器与关系抽取相结合，通过预训练金融数据集来适应金融领域任务，其独特的结构和输入模式可以提高两个实体之间关系的准确性，在大规模金融关系抽取数据集 REFinD 上表现出色。