In this paper, we examine several methods of acquiring Czech data for
automated fact-checking, which is a task commonly modeled as a classification
of textual claim veracity w.r.t. a corpus of trusted ground truths. We attempt
to collect sets of data in form of a factual claim, evidence within the ground
truth corpus, and its veracity label (supported, refuted or not enough info).
As a first attempt, we generate a Czech version of the large-scale FEVER
dataset built on top of Wikipedia corpus. We take a hybrid approach of machine
translation and document alignment; the approach and the tools we provide can
be easily applied to other languages. We discuss its weaknesses and
inaccuracies, propose a future approach for their cleaning and publish the 127k
resulting translations, as well as a version of such dataset reliably
applicable for the Natural Language Inference task - the CsFEVER-NLI.
Furthermore, we collect a novel dataset of 3,097 claims, which is annotated
using the corpus of 2.2M articles of Czech News Agency. We present its extended
annotation methodology based on the FEVER approach, and, as the underlying
corpus is kept a trade secret, we also publish a standalone version of the
dataset for the task of Natural Language Inference we call CTKFactsNLI. We
analyze both acquired datasets for spurious cues - annotation patterns leading
to model overfitting. CTKFacts is further examined for inter-annotator
agreement, thoroughly cleaned, and a typology of common annotator errors is
extracted. Finally, we provide baseline models for all stages of the
fact-checking pipeline and publish the NLI datasets, as well as our annotation
platform and other experimental data.

本文介绍了获取捷克语数据进行自动事实核查的方法，其中提出了一种混合机器翻译和文档对齐的方法来生成一个基于 Wikipedia 的大规模 FEVER 数据集的捷克语版本，以及一种可靠应用于自然语言推理任务的 CsFEVER-NLI 版本。同时，还介绍了使用捷克新闻机构语料库对一些数据进行扩展注释，以及该数据集的基线模型和注释平台等实验数据。