We introduce CORE, a dataset for few-shot relation classification (RC)
focused on company relations and business entities. CORE includes 4,708
instances of 12 relation types with corresponding textual evidence extracted
from company Wikipedia pages. Company names and business entities pose a
challenge for few-shot RC models due to the rich and diverse information
associated with them. For example, a company name may represent the legal
entity, products, people, or business divisions depending on the context.
Therefore, deriving the relation type between entities is highly dependent on
textual context. To evaluate the performance of state-of-the-art RC models on
the CORE dataset, we conduct experiments in the few-shot domain adaptation
setting. Our results reveal substantial performance gaps, confirming that
models trained on different domains struggle to adapt to CORE. Interestingly,
we find that models trained on CORE showcase improved out-of-domain
performance, which highlights the importance of high-quality data for robust
domain adaptation. Specifically, 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. In addition to the
dataset, we provide relevant code snippets to facilitate reproducibility and
encourage further research in the field.

通过介绍 CORE 数据集，探讨了少样本关系分类和领域适应的性能差异，发现基于 CORE 数据集训练的模型在跨领域表现得较好，强调了数据丰富性对领域适应的重要性。

CORE: 用于鲁棒领域适应的少样本企业关系分类数据集

CORE: A Few-Shot Company Relation Classification Dataset for Robust  Domain Adaptation

The price movement prediction of stock market has been a classical yet
challenging problem, with the attention of both economists and computer
scientists. In recent years, graph neural network has significantly improved
the prediction performance by employing deep learning on company relations.
However, existing relation graphs are usually constructed by handcraft human
labeling or nature language processing, which are suffering from heavy resource
requirement and low accuracy. Besides, they cannot effectively response to the
dynamic changes in relation graphs. Therefore, in this paper, we propose a
temporal and heterogeneous graph neural network-based (THGNN) approach to learn
the dynamic relations among price movements in financial time series. In
particular, we first generate the company relation graph for each trading day
according to their historic price. Then we leverage a transformer encoder to
encode the price movement information into temporal representations. Afterward,
we propose a heterogeneous graph attention network to jointly optimize the
embeddings of the financial time series data by transformer encoder and infer
the probability of target movements. Finally, we conduct extensive experiments
on the stock market in the United States and China. The results demonstrate the
effectiveness and superior performance of our proposed methods compared with
state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a
real-world quantitative algorithm trading system, the accumulated portfolio
return obtained by our method significantly outperforms other baselines.

本论文提出了一种基于时间和异构图神经网络（THGNN）的方法，用于学习金融时间序列中价格变动之间的动态关系。该方法在美国和中国的股票市场上展开广泛实验，并在实际交易中实现了显著的累积组合回报。