Recent approaches to automatically detect the speaker of an utterance of
direct speech often disregard general information about characters in favor of
local information found in the context, such as surrounding mentions of
entities. In this work, we explore stylistic representations of characters
built by encoding their quotes with off-the-shelf pretrained Authorship
Verification models in a large corpus of English novels (the Project Dialogism
Novel Corpus). Results suggest that the combination of stylistic and topical
information captured in some of these models accurately distinguish characters
among each other, but does not necessarily improve over semantic-only models
when attributing quotes. However, these results vary across novels and more
investigation of stylometric models particularly tailored for literary texts
and the study of characters should be conducted.

通过对大量英语小说进行预训练的作者验证模型编码以角色的引用，本研究探索了角色的文体表征，并且结果表明这些模型中融合的文体和主题信息能够准确区分角色，但在归属引用方面并没有必要超过仅有语义的模型，然而这些结果在小说之间存在差异，因此在文学文本和角色研究领域需要进行更多针对性的文体测量模型的研究。

区分虚构声音：引语归属的作者验证模型研究

Distinguishing Fictional Voices: a Study of Authorship Verification  Models for Quotation Attribution

The automatic annotation of direct speech (AADS) in written text has been
often used in computational narrative understanding. Methods based on either
rules or deep neural networks have been explored, in particular for English or
German languages. Yet, for French, our target language, not many works exist.
Our goal is to create a unified framework to design and evaluate AADS models in
French. For this, we consolidated the largest-to-date French narrative dataset
annotated with DS per word; we adapted various baselines for sequence labelling
or from AADS in other languages; and we designed and conducted an extensive
evaluation focused on generalisation. Results show that the task still requires
substantial efforts and emphasise characteristics of each baseline. Although
this framework could be improved, it is a step further to encourage more
research on the topic.

本研究旨在为法语语言创建统一的框架，以设计和评估直接言语的自动标注模型，探索了基于规则或深度神经网络的方法，并设计了全面评估以便针对泛化提供更好的方案。