  Character linking, the task of linking mentioned people in conversations to
the real world, is crucial for understanding the conversations. For the
efficiency of communication, humans often choose to use pronouns (e.g., "she")
or normal phrases (e.g., "that girl") rather than named entities (e.g.,
"Rachel") in the spoken language, which makes linking those mentions to real
people a much more challenging than a regular entity linking task. To address
this challenge, we propose to incorporate the richer context from the
coreference relations among different mentions to help the linking. On the
other hand, considering that finding coreference clusters itself is not a
trivial task and could benefit from the global character information, we
propose to jointly solve these two tasks. Specifically, we propose C$^2$, the
joint learning model of Coreference resolution and Character linking. The
experimental results demonstrate that C$^2$ can significantly outperform
previous works on both tasks. Further analyses are conducted to analyze the
contribution of all modules in the proposed model and the effect of all
hyper-parameters.
