I've been a freelance journalist for 10 years, usually writing for magazines and websites about cinema. I presented a morning show on Radio Kraków twice a week for about two years. It was only one part of my work, but I really enjoyed it. It was about culture and cinema, and featured a range of people, from artists to activists. I remember interviewing Ukrainians about the Russian invasion for the first programme I presented, back in 2022. I was let go in August 2024, alongside a dozen co-workers who were also part-time. We were told the radio station was having financial problems.

The DocRED dataset is one of the most popular and widely used benchmarks for document-level relation extraction (RE). It adopts a recommend-revise annotation scheme so as to have a large-scale annotated dataset. However, we find that the annotation of DocRED is incomplete, i.e., false negative samples are prevalent. We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset. To address the shortcoming, we re-annotate 4,053 documents in the DocRED dataset by adding the missed relation triples back to the original DocRED. We name our revised DocRED dataset Re-DocRED. We conduct extensive experiments with state-of-the-art neural models on both datasets, and the experimental results show that the models trained and evaluated on our Re-DocRED achieve performance improvements of around 13 F1 points. Moreover, we conduct a comprehensive analysis to identify the potential areas for further improvement. Our dataset is publicly available at https://github.com/tonytan48/Re-DocRED.