Humans naturally attribute utterances of direct speech to their speaker in
literary works. When attributing quotes, we process contextual information but
also access mental representations of characters that we build and revise
throughout the narrative. Recent methods to automatically attribute such
utterances have explored simulating human logic with deterministic rules or
learning new implicit rules with neural networks when processing contextual
information. However, these systems inherently lack \textit{character}
representations, which often leads to errors on more challenging examples of
attribution: anaphoric and implicit quotes. In this work, we propose to augment
a popular quotation attribution system, BookNLP, with character embeddings that
encode global information of characters. To build these embeddings, we create
DramaCV, a corpus of English drama plays from the 15th to 20th century focused
on Character Verification (CV), a task similar to Authorship Verification (AV),
that aims at analyzing fictional characters. We train a model similar to the
recently proposed AV model, Universal Authorship Representation (UAR), on this
dataset, showing that it outperforms concurrent methods of characters
embeddings on the CV task and generalizes better to literary novels. Then,
through an extensive evaluation on 22 novels, we show that combining BookNLP's
contextual information with our proposed global character embeddings improves
the identification of speakers for anaphoric and implicit quotes, reaching
state-of-the-art performance. Code and data will be made publicly available.

我们提出利用字符嵌入对一种叫做 BookNLP 的流行引文归属系统进行增强，这些嵌入编码了字符的全局信息，通过与上下文信息相结合，提高了对指代和隐含引用的讲话者的识别能力，达到了最先进的性能水平。

利用虚构角色嵌入提高引用归属

Improving Quotation Attribution with Fictional Character Embeddings

Low-resource named entity recognition is still an open problem in NLP. Most
state-of-the-art systems require tens of thousands of annotated sentences in
order to obtain high performance. However, for most of the world's languages,
it is unfeasible to obtain such annotation. In this paper, we present a
transfer learning scheme, whereby we train character-level neural CRFs to
predict named entities for both high-resource languages and low resource
languages jointly. Learning character representations for multiple related
languages allows transfer among the languages, improving F1 by up to 9.8 points
over a loglinear CRF baseline.

通过字符级神经 CRF 模型进行跨多个语言的命名实体识别的迁移学习，使得在资源丰富和资源匮乏的语言中都能提高 F1 分数，并在基线上提升 9.8 个点。