Style representation learning builds content-independent representations of
author style in text. Stylometry, the analysis of style in text, is often
performed by expert forensic linguists and no large dataset of stylometric
annotations exists for training. Current style representation learning uses
neural methods to disentangle style from content to create style vectors,
however, these approaches result in uninterpretable representations,
complicating their usage in downstream applications like authorship attribution
where auditing and explainability is critical. In this work, we use prompting
to perform stylometry on a large number of texts to create a synthetic dataset
and train human-interpretable style representations we call LISA embeddings. We
release our synthetic stylometry dataset and our interpretable style models as
resources.

本研究使用提示技术，对大量文本进行文体分析，得到了一组可解释的文体表示方法 LISA embeddings，为解决当前使用神经网络进行文体表示学习所面临的可解释性困境提供了新的解决方案。

通过提示 LLMs 学习可解释的风格嵌入

Learning Interpretable Style Embeddings via Prompting LLMs

Expressive speech synthesis, like audiobook synthesis, is still challenging
for style representation learning and prediction. Deriving from reference audio
or predicting style tags from text requires a huge amount of labeled data,
which is costly to acquire and difficult to define and annotate accurately. In
this paper, we propose a novel framework for learning style representation from
abundant plain text in a self-supervised manner. It leverages an emotion
lexicon and uses contrastive learning and deep clustering. We further integrate
the style representation as a conditioned embedding in a multi-style
Transformer TTS. Comparing with multi-style TTS by predicting style tags
trained on the same dataset but with human annotations, our method achieves
improved results according to subjective evaluations on both in-domain and
out-of-domain test sets in audiobook speech. Moreover, with implicit
context-aware style representation, the emotion transition of synthesized audio
in a long paragraph appears more natural. The audio samples are available on
the demo web.

本文提出了一种利用自我监督学习从大量纯文本中学习风格表达的新框架，采用情感词典和对比学习和深度聚类，最终在多风格变换文本到语音系统中得到了更好的结果。