# FREDC: A Few-Shot Relation Extraction Dataset for Chinese
## 2 Related Work  
The quantity and quality of datasets are pivotal in the progression of RE. The research conducted by [PERSON] [16] on RE datasets has revealed a significant evolution in the target domains over the past five years. Initially focused on news texts, the emphasis has shifted to web texts, and more recently to scientific publications and Wikipedia. Concurrently, there is an increasing emphasis on multilingual and few-shot methodologies. There are currently approximately 17 mainstream datasets, but datasets specifically tailored for few-shot RE are actually very scarce. From our survey, we can see that that the majority of datasets for few-shot tasks are based on English. Furthermore, researchers approach dataset construction with two main objectives: to ensure the quality of the dataset and to introduce new challenges through it. Quality assurance involves focusing on the volume of data and the accuracy of annotations. Introducing challenges involves enriching data diversity and increasing the complexity of identification, thereby enhancing the ability of models to address real-world problems. The first large-scale dataset introduced for English few-shot RE research is FewRel 1.0, which has become the most frequently used dataset in the field. Developed by [PERSON] et al. [12] using the Wikipedia corpus, FewRel 1.0 encompasses nearly a hundred relation types and tens of thousands of high-quality annotated data, identified through distant supervision and refined by crowd-workers. Building upon this, [PERSON] et al. [17] introduced FewRel 2.0, an enhanced and expanded version of FewRel 1.0. It includes a new test subset from various domains and introduces NOTA, enabling further investigation into whether models can adapt to new fields with limited examples or recognize new relations (NOTA). Recently, [PERSON] [18] presented CORE, a few-shot RE dataset focusing on corporate relations and entities. In the dataset, the information richness embedded in business entities allows models to focus on contextual nuances, reducing their reliance on superficial clues such as relation-specific verbs, which improves the model's ability of cross-domain RE. Besides datasets specifically designed for few-shot RE tasks, there are also some few-shot RE datasets that are generated through adaptations of supervised RE datasets. [PERSON] et al. [19] argued that the evaluation framework of FewRel is an unrealistic benchmark. Thereby, they propose a novel methodology for deriving more realistic few-shot test data from datasets for supervised RE, which converted the supervised TACRED [11] into a few-shot TACRED variant by applying realistic episode sampling. [PERSON] et al. [20] argue that document-level corpora provide more realism, particularly regarding NOTA distributions. Therefore, they present FREDo, a few-shot document-level RE (FSDLRE) benchmark. The FREDo used the documents in the development corpus of DocRED [21] as the test corpus for our in-domain task. The DocRED training corpus is therefore used as the basis for both our training and development set. sciERC [22] is used as our cross-domain test subset. When compared to the extensive availability of English few-shot RE datasets, the Chinese counterparts remain significantly scarce. The majority of Chinese few-shot RE datasets are constructed by researchers for their individual studies. At present, the research in the field of Chinese few-shot RE is primarily focused on addressing the issues within existing few-shot RE models and exploring new approaches for Chinese. [PERSON] et al. [13] built a Chinese dataset based on the FinRE [23] and sought to improve model performance with noisy data. FinRE contains data from 2647 instances of financial news, with 13,486,3727 and 1489 relation instances for training, testing and validation, respectively. FinRE contains 44 relationships including a special relation NA, which indicates that there is no relation between the marked entity pair.