PolQA: Polish Question Answering Dataset
CoRR(2022)
摘要
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 accuracy@10 by 10.55 p.p. while reducing the annotation cost
by 82
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关键词
question answering performance,manual annotation
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