  While there has been substantial progress in developing systems to automate
fact-checking, they still lack credibility in the eyes of the users. Thus, an
interesting approach has emerged: to perform automatic fact-checking by
verifying whether an input claim has been previously fact-checked by
professional fact-checkers and to return back an article that explains their
decision. This is a sensible approach as people trust manual fact-checking, and
as many claims are repeated multiple times. Yet, a major issue when building
such systems is the small number of known tweet--verifying article pairs
available for training. Here, we aim to bridge this gap by making use of crowd
fact-checking, i.e., mining claims in social media for which users have
responded with a link to a fact-checking article. In particular, we mine a
large-scale collection of 330,000 tweets paired with a corresponding
fact-checking article. We further propose an end-to-end framework to learn from
this noisy data based on modified self-adaptive training, in a distant
supervision scenario. Our experiments on the CLEF'21 CheckThat! test set show
improvements over the state of the art by two points absolute. Our code and
datasets are available at https://github.com/mhardalov/crowdchecked-claims
