Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention span data as well as other entity information. This paper presents a coreference model that learns singletons as well as features such as entity type and information status via a multi-task learning-based approach. This approach achieves new state-of-the-art scores on the OntoGUM benchmark (+2.7 points) and increases robustness on multiple out-of-domain datasets (+2.3 points on average), likely due to greater generalizability for mention detection and utilization of more data from singletons when compared to only coreferent mention pair matching.

翻译：先前将指称检测步骤整合到端到端神经共指消解模型中的尝试，因缺乏单例指称跨度数据及其他实体信息而受到制约。本文提出一种基于多任务学习的共指消解模型，该模型能同时学习单例及实体类型、信息状态等特征。该方法在OntoGUM基准测试上取得最新最优成绩（+2.7分），并在多个域外数据集上增强了模型鲁棒性（平均提升2.3分），这很可能归因于与仅依赖共指指称对匹配相比，该模型在指称检测方面具有更强的泛化能力，且能更充分利用单例数据。