As Deep Learning architecture, we decided to use c2f (Lee et al., 2018)<ref xmlns="http://www.tei-c.org/ns/1.0" target="ftn2" n="2" />. It is based on e2e (Lee et al., 2017), which was the first end-to-end neural network-based architecture for coreference resolution. e2e begins by building span representations for all spans up to a pre-defined length. All span representations are scored by a feed-forward neural network and only the top-scoring spans are kept (usually about 40% of all spans), all others are discarded. Each remaining span representation is then paired with a pre-defined number of potential antecedents and the pairs are scored by another feed-forward neural network (aside from the span representations the feed forward neural network (FFNN) also receives additional information like the domain of the document and whether both spans have the same speaker). Since not all span representations actually have an antecedent they are also paired with a dummy antecedent. For each span, the highest-scoring partner is picked as antecedent (unless the dummy antecedent was the highest-scoring partner, then the span does not have an antecedent). The architecture of c2f extends e2e in several ways, the two most important are the following: First, it uses a coarse bilinear scoring function, which is easier to compute, to prune the span representations before they are scored by the FFNN. Secondly, it scores the span representation pairs more than once and refines the span representations based on the scoring results between the iterations.