We present in this work a new dataset of coreference annotations for works of
literature in English, covering 29,103 mentions in 210,532 tokens from 100
works of fiction. This dataset differs from previous coreference datasets in
containing documents whose average length (2,105.3 words) is four times longer
than other benchmark datasets (463.7 for OntoNotes), and contains examples of
difficult coreference problems common in literature. This dataset allows for an
evaluation of cross-domain performance for the task of coreference resolution,
and analysis into the characteristics of long-distance within-document
coreference.

本文提出了一个新的数据集，其中包含 100 部英文小说的 29,103 个指代注释，涵盖了 210,532 个标记。这个数据集对比以前的数据集不同的地方在于包含了平均长度为 2,105.3 个单词的文档，是其他基准数据集的四倍长（OntoNotes 为 463.7），并且包含了文学中常见的难度指代问题的示例。这个数据集可以评估指代消解任务的跨领域性能，并分析长距离文档内指代的特征。

英文文学代词消解的已注释数据集

An Annotated Dataset of Coreference in English Literature

Many problems in NLP require aggregating information from multiple mentions
of the same entity which may be far apart in the text. Existing Recurrent
Neural Network (RNN) layers are biased towards short-term dependencies and
hence not suited to such tasks. We present a recurrent layer which is instead
biased towards coreferent dependencies. The layer uses coreference annotations
extracted from an external system to connect entity mentions belonging to the
same cluster. Incorporating this layer into a state-of-the-art reading
comprehension model improves performance on three datasets -- Wikihop, LAMBADA
and the bAbi AI tasks -- with large gains when training data is scarce.

本文提出了一种针对 NLP 中需要汇总来自远处的同一实体的信息的情况的循环层，该层使用外部系统提取的指代注释连接属于同一簇的实体提及，并将其合并到最先进的阅读理解模型中，从而改善了 Wikihop、LAMBADA 和 bAbi 人工智能任务的性能，并且可在数据稀缺时获得大的收益。