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

arXiv: https://arxiv.org/abs/2401.16968 · HTML: https://mlsanity.com/paper/2401.16968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [Measuring Information Propagation in Literary Social Networks](https://mlsanity.com/paper/2004.13980/) (2020)
- [Multi-level Contrastive Learning for Script-based Character Understanding](https://mlsanity.com/paper/2310.13231/) (2023)
- [The Project Dialogism Novel Corpus: A Dataset for Quotation Attribution in Literary Texts](https://mlsanity.com/paper/2204.05836/) (2022)
- [Improving Automatic Quotation Attribution in Literary Novels](https://mlsanity.com/paper/2307.03734/) (2023)

## Built on by (most cited first, 4 of 4)

- [Improving Quotation Attribution with Fictional Character Embeddings](https://mlsanity.com/paper/2406.11368/) (2024)
- [Computational Narrative Understanding for Expressive Text-to-Speech](https://mlsanity.com/paper/2509.04072/) (2025)
- [Capturing Classic Authorial Style in Long-Form Story Generation with GRPO Fine-Tuning](https://mlsanity.com/paper/2512.05747/) (2025)
- [Fast and Accurate Quotation Attribution in Literary Texts](https://mlsanity.com/paper/2608.02359/) (2026)

## Abstract (arXiv)

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.