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The shift towards publicly available text sources has enabled languageprocessing at unprecedented scale, yet leaves under-serviced the domains wherepublic and openly licensed data is scarce. Proactively collecting text data forresearch is a viable strategy to address this scarcity, but lacks systematicmethodology taking into account the many ethical, legal andconfidentiality-related aspects of data collection. Our work presents a casestudy on proactive data collection in peer review -- a challenging andunder-resourced NLP domain. We outline ethical and legal desiderata forproactive data collection and introduce "Yes-Yes-Yes", the first donation-basedpeer reviewing data collection workflow that meets these requirements. Wereport on the implementation of Yes-Yes-Yes at ACL Rolling Review andempirically study the implications of proactive data collection for the datasetsize and the biases induced by the donation behavior on the peer reviewingplatform.

Yes-Yes-Yes: Proactive Data Collection for ACL Rolling Review and Beyond