In recent years, great advances in pre-trained language models (PLMs) have
sparked considerable research focus and achieved promising performance on the
approach of dense passage retrieval, which aims at retrieving relative passages
from massive corpus with given questions. However, most of existing datasets
mainly benchmark the models with factoid queries of general commonsense, while
specialised fields such as finance and economics remain unexplored due to the
deficiency of large-scale and high-quality datasets with expert annotations. In
this work, we propose a new task, policy retrieval, by introducing the Chinese
Stock Policy Retrieval Dataset (CSPRD), which provides 700+ prospectus passages
labeled by experienced experts with relevant articles from 10k+ entries in our
collected Chinese policy corpus. Experiments on lexical, embedding and
fine-tuned bi-encoder models show the effectiveness of our proposed CSPRD yet
also suggests ample potential for improvement. Our best performing baseline
achieves 56.1% MRR@10, 28.5% NDCG@10, 37.5% Recall@10 and 80.6% Precision@10 on
dev set.

近年来，预训练语言模型（PLMs）取得了巨大进展，在密集文段检索方法上取得了令人满意的性能，旨在通过给定的问题从大规模语料库中检索相关文段。然而，现有数据集大多采用常识事实查询对模型进行基准测试，而金融和经济等专业领域由于缺乏大规模高质量的专家注释数据集而未被深入研究。本研究提出了一项新任务，即政策检索，通过引入中文股票政策检索数据集（CSPRD），该数据集由经验丰富的专家使用我们收集的中国政策语料库中的相关文章对 700 多个招股说明书文段进行了标注。对词汇、嵌入和经过微调的双编码模型进行的实验表明了我们提出的 CSPRD 的有效性，同时也显示出了进一步改进的潜力。我们最佳的基准模型在开发集上达到了 56.1％的 MRR@10，28.5％的 NDCG@10，37.5％的 Recall@10 和 80.6％的 Precision@10。