# Identifying Speakers and Addressees of Quotations in Novels with Prompt Learning

arXiv: https://arxiv.org/abs/2408.09452 · HTML: https://mlsanity.com/paper/2408.09452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Measuring Information Propagation in Literary Social Networks](https://mlsanity.com/paper/2004.13980/) (2020)
- [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)

## Abstract (arXiv)

Quotations in literary works, especially novels, are important to create characters, reflect character relationships, and drive plot development. Current research on quotation extraction in novels primarily focuses on quotation attribution, i.e., identifying the speaker of the quotation. However, the addressee of the quotation is also important to construct the relationship between the speaker and the addressee. To tackle the problem of dataset scarcity, we annotate the first Chinese quotation corpus with elements including speaker, addressee, speaking mode and linguistic cue. We propose prompt learning-based methods for speaker and addressee identification based on fine-tuned pre-trained models. Experiments on both Chinese and English datasets show the effectiveness of the proposed methods, which outperform methods based on zero-shot and few-shot large language models.