<note xmlns="http://www.tei-c.org/ns/1.0" place="foot" xml:id="ftn1" n="1"> We use only 46 tales because the other documents have not been annotated yet. The data used can be found at https://gitlab.informatik.uni-wuerzburg.de/kallimachos/coref-adaptation</note><note xmlns="http://www.tei-c.org/ns/1.0" place="foot" xml:id="ftn2" n="2"> Most other NN architectures require even more memory and time to train. We also spent some effort experimenting with a more memory-efficient architecture (Kirstain et al., 2021) but could not get any substantial results.</note><note xmlns="http://www.tei-c.org/ns/1.0" place="foot" xml:id="ftn3" n="3"> Since we do not have any GPUs with sufficient memory capacity (more than 24 GB) c2f D was trained on CPUs, which took about one week. Training on the fairy tales was done on an RTX3090 in a few hours.</note><note xmlns="http://www.tei-c.org/ns/1.0" place="foot" xml:id="ftn4" n="4"> https://github.com/t-systems-on-site-services-gmbh/german-elmo-model</note>