There are several recent works that evaluate the performance of coreference resolution models when applied to a different domain than they have been trained on. Srivastava et al. (2018) examine the performance of several coreference resolution systems (rule-based, statistical and projection-based) on English and German out-of-domain data and find that the rule-based system is the best choice for their use cases. Han et al. (2021) train a coreference resolution model based on c2f (Lee et al., 2018) and SpanBERT (Joshi et al., 2020) on two different corpora, Ontonotes (Hovy et al., 2006) and their new corpus FantasyCoref. They then evaluate both models on FantasyCoref and find that the model trained on the same domain outperforms the other one. (Toshniwal et al., 2021) examine the generalization capabilities of coreference models by evaluating the performance of longdoc (Toshniwal et al., 2020) on out-of-domain data using several English datasets and find that models which have been trained on several datasets jointly perform better than those trained on a single dataset.