Authorship verification is the problem of determining if two distinct writing
samples share the same author and is typically concerned with the attribution
of written text. In this paper, we explore the attribution of transcribed
speech, which poses novel challenges. The main challenge is that many stylistic
features, such as punctuation and capitalization, are not available or
reliable. Therefore, we expect a priori that transcribed speech is a more
challenging domain for attribution. On the other hand, other stylistic
features, such as speech disfluencies, may enable more successful attribution
but, being specific to speech, require special purpose models. To better
understand the challenges of this setting, we contribute the first systematic
study of speaker attribution based solely on transcribed speech. Specifically,
we propose a new benchmark for speaker attribution focused on conversational
speech transcripts. To control for spurious associations of speakers with
topic, we employ both conversation prompts and speakers' participating in the
same conversation to construct challenging verification trials of varying
difficulties. We establish the state of the art on this new benchmark by
comparing a suite of neural and non-neural baselines, finding that although
written text attribution models achieve surprisingly good performance in
certain settings, they struggle in the hardest settings we consider.

论文通过研究转写语音来探讨作者识别的问题，重点在于解决转写语音中特有的挑战性，包括控制主题相关性和基于转写语音构建的 speaker attribution 基准测试。通过与神经网络和非神经网络模型进行对比，发现尽管书面文本的作者识别模型在某些情况下表现出令人惊讶的性能，但在考虑的最难的情景中仍然存在困难。

作者归属模型能否识别演讲记录中的讲话者？

Can Authorship Attribution Models Distinguish Speakers in Speech  Transcripts?

Current models for quotation attribution in literary novels assume varying
levels of available information in their training and test data, which poses a
challenge for in-the-wild inference. Here, we approach quotation attribution as
a set of four interconnected sub-tasks: character identification, coreference
resolution, quotation identification, and speaker attribution. We benchmark
state-of-the-art models on each of these sub-tasks independently, using a large
dataset of annotated coreferences and quotations in literary novels (the
Project Dialogism Novel Corpus). We also train and evaluate models for the
speaker attribution task in particular, showing that a simple sequential
prediction model achieves accuracy scores on par with state-of-the-art models.

当前文献关注文学小说中引文归属问题，以引文归属为中心，研究四个相互关联的子任务：人物识别、指代消解、引文辨别和说话人归属。在大量注释了核心指代和引文的文学小说数据集（项目言对小说语料库）上评估了最先进的模型，并针对说话人归属任务进行训练和评估，表明一个简单的连续预测模型具有与最先进模型相当的准确性分数。

提升文学小说中自动引用标注的准确性

Improving Automatic Quotation Attribution in Literary Novels

We present the task of modeling information propagation in literature, in
which we seek to identify pieces of information passing from character A to
character B to character C, only given a description of their activity in text.
We describe a new pipeline for measuring information propagation in this domain
and publish a new dataset for speaker attribution, enabling the evaluation of
an important component of this pipeline on a wider range of literary texts than
previously studied. Using this pipeline, we analyze the dynamics of information
propagation in over 5,000 works of fiction, finding that information flows
through characters that fill structural holes connecting different communities,
and that characters who are women are depicted as filling this role much more
frequently than characters who are men.

我们提出了文学中信息传播建模任务，并使用一种新的流程在超过 5000 部小说中分析信息传播动态，发现填补不同社群之间结构空缺的女性角色比男性角色更常见