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。