We present an effective system adapted from the end-to-end neural coreference resolution model, targeting on the task of anaphora resolution in dialogues. Three aspects are specifically addressed in our approach, including the support of singletons, encoding speakers and turns throughout dialogue interactions, and knowledge transfer utilizing existing resources. Despite the simplicity of our adaptation strategies, they are shown to bring significant impact to the final performance, with up to 27 F1 improvement over the baseline. Our final system ranks the 1st place on the leaderboard of the anaphora resolution track in the CRAC 2021 shared task, and achieves the best evaluation results on all four datasets.

翻译：我们提出了一个从端到端神经共同参照解决方案模式改编而成的有效系统,以对话中解决厌光动物问题的任务为目标,我们的方法具体涉及三个方面,包括支持单子体、编码发言者和在整个对话互动过程中转换,以及利用现有资源的知识转让。尽管我们的适应战略简单,但事实证明它们给最终业绩带来重大影响,比基线改进了27个F1。我们的最后系统在CRAC 2021年共同任务中将厌光动物问题解决方案轨道的首选位置排在CRAC 2021年共同任务中,并取得了所有四个数据集的最佳评价结果。