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.

这篇论文提出了一种通过多任务学习方法学习单例以及实体类型和信息状态等特征的共指模型，在 OntoGUM 基准上取得了新的最高分（+2.7 分），并在多个领域的数据集上提高了鲁棒性（平均 + 2.3 分），可能是由于更大的指称检测的泛化能力和更多单例数据的利用所致。

利用多任务学习将单例和基于提及的特征纳入共指消解，以提高泛化性能

Incorporating Singletons and Mention-based Features in Coreference  Resolution via Multi-task Learning for Better Generalization

We adapt Lee et al.'s (2018) span-based entity coreference model to the task
of end-to-end discourse deixis resolution in dialogue, specifically by
proposing extensions to their model that exploit task-specific characteristics.
The resulting model, dd-utt, achieves state-of-the-art results on the four
datasets in the CODI-CRAC 2021 shared task.

通过对任务特定特征进行扩展，我们将 Lee 等人（2018）的基于跨度的实体共指模型应用于对话中的端到端篇章指代解析任务，并提出了 dd-utt 模型。该模型在 CODI-CRAC 2021 共享任务的四个数据集上取得了最先进的结果。