Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is known to be time-consuming and therefore expensive to acquire. As a result, the appropriate datasets are available only for a handful of languages (mainly English and Chinese). In this work, we introduce and publicly release PolQA, the first Polish dataset for OpenQA. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7,097,322 candidate passages. Each question is classified according to its formulation, type, as well as entity type of the answer. This resource allows us to evaluate the impact of different annotation choices on the performance of the QA system and propose an efficient annotation strategy that increases the passage retrieval performance by 10.55 p.p. while reducing the annotation cost by 82%.

本文介绍并公开发布了PolQA，这是第一个用于OpenQA的波兰语数据集。该数据集包含7,000个问题，87,525个手动标注的证据段落和超过7,097,322个候选段落。该资源允许我们评估不同注释选择对QA系统性能的影响，并提出一种有效的注释策略，使召回率提高了10.55 p.p. 并同时将注释成本降低了82％。

通过手动注释提高问答性能：成本、效益和策略