# FREDC: A Few-Shot Relation Extraction Dataset for Chinese
## 1 Introduction  
In the field of natural language processing (NLP), relation extraction (RE) represents a significant research domain. The primary objective of RE is to empower models to discern the attributes of relation categories through the analysis of training data, thereby facilitating the identification of relations between entities within the textual context. As depicted in Figure 1, both supervised RE [1, 2, 3, 4] and few-shot RE [5, 6, 7, 8] are key methodologies. In the case of supervised RE, models can learn the features of the relation types directly from the training data. In contrast, few-shot RE leverages a limited quantity of labeled data from the support set for training or fine-tuning, which requires the models to accurately classify categories with only a few available samples. Consequently, the construction of few-shot RE datasets is more challenging and complex, involving the selection of samples that not only require a sufficient number, but also ensure variety to generalize effectively. Currently, there are dozens of widely used supervised RE datasets, such as CoNLL04 [9], SemEval-2010 Task 8 [10], TACRED [11], etc. However, for few-shot RE, the most widely used dataset is FewRel [12]. Moreover, current publicly available few-shot RE datasets are primarily focused on English, resulting in limited research on few-shot RE for other languages. In Chinese few-shot RE research [13, 14, 15, 16], the datasets used in experiments are typically constructed individually, leading to significant variations between them. This lack of consistency has resulted in the absence of a standardized evaluation framework. To address these challenges, we introduce a few-shot RE dataset for Chinese (FREDC), which encompasses nearly 100 types of relations across general, medical, and financial domains. By integrating and optimizing existing high-quality Chinese information extraction datasets, FREDC is designed to meet the diverse needs of few-shot RE research, including tasks such as cross-domain and \"None-of-the-Above\" (NOTA), as defined in the FewRel benchmark. Unlike previous Chinese few-shot RE datasets, FREDC is not restricted to a specific research setup. Additionally, in experiments related to cross-domain, the complexity of Chinese data in the economic and medical fields makes it difficult for models to determine relations based solely on entity labels, adding to the challenge of the FREDC dataset. To validate the effectiveness of FREDC, we made structural adjustments to several few-shot models that were originally designed for English, enabling them to process Chinese text to some extent. We conducted a series of experiments using these models, and comparative results showed that FREDC yields effective results across various few-shot RE settings. FREDC presents certain challenges for models compared to English datasets. In our experiments, we use accuracy as a performance evaluation metric. The maximum accuracy difference is about 30% in complex tasks, such as cross-domain and NOTA tasks. Furthermore, our experiments also demonstrate that as the number of samples of the support set increases, the impact of language differences between Chinese and English gradually diminishes. The contributions of this article are as follows:  
* We presented the FREDC, which is built from Chinese linguistic resources and specifically designed for the purpose of few-shot RE. It encompasses nearly a hundred distinct relational categories and incorporates specialized knowledge from multiple domains, thereby substantially mitigating the scarcity of publicly accessible Chinese datasets for few-shot RE. * We have aligned the format of FREDC with the current mainstream datasets, allowing it to be directly applied to various few-shot task settings, such as cross-domain and  
Figure 1: The main process of supervised RE and few-shot RE. NOTA. Additionally, experimental verification has been conducted to demonstrate the effectiveness of the dataset.