Automatic Quote Attribution in C hinese Literary Works (2024)

Abstract: Quote attribution in fiction refers to the extraction of dialogues and speaker identification of dialogues, which can be divided into 2 steps: quotation annotation and speaker annotation. We use a pipeline for quote attribution, which involves classification, extractive QA, multi-choice QA, and coreference resolution. We also had an evaluation of our model performance by predicting explicit and implicit speakers using a combination of different models.

## introduction quote attribution, within the realm of literature and textual analysis, plays a pivotal role in enhancing the clarity and understanding of dialogues. it involves the extraction of dialogues from a text and the subsequent identification of the speakers involved. by assigning the appropriate speakers to their respective utterances, quote attribution enables readers to follow the flow of conversation and comprehend the nuances of a narrative. while manual quote attribution has been the traditional approach, advancements in natural language processing and machine learning have opened up exciting possibilities for automating this process. in this article, we explore the challenges and techniques associated with quote attribution, as well as the significance of automated methods in facilitating efficient and accurate analysis of dialogues in various literary contexts. we leverage an annotated dataset consisting of 1991 modern chinese novels as the foundation of our research. while our primary focus is on modern chinese literature, our methodology can be seamlessly applied to other languages as well, despite potential language differences. initially, our approach revolves around treating quote attribution as an extractive question-answering (qa) problem. to accomplish this, we fine-tune a pre-trained bert model specifically for dialogues within fictional texts. as we delve deeper into the complexities of quote attribution, we address more intricate scenarios, such as anaphora and the continuity of conversational threads @xcite . to effectively handle the resolution of conversational threads, we employ a pre-trained bert model designed for multi-choice qa. furthermore, we utilize co-reference resolution techniques to tackle anaphoric references within the dialogues. in order to distinguish between crowds, soliloquies, and dialogues, we employ a combination of rule-based filtering and a bert-based classifier. this hybrid approach enables us to accurately identify and categorize different speech patterns and formats within the text. our research encompasses several contributions, which can be summarized as follows: ## background/related work to perform quote attribution, a dataset containing quoted speech from literary texts is essential for training, evaluation, and testing models. several studies have focused primarily on creating datasets specifically for quote attribution. there are some open-source datasets available in this field, most of which are english literature works. @xcite developed an annotation tool for quotation annotation and created the quoteli3 dataset with speaker and quotation annotations in 3 novels @xcite . similarly, sims, matthew, et al. presented datasets for speaker attribution, comprised of 1,765 quotations linked to their speakers in 100 different literary texts @xcite , which is now part of the litbank corpus. the pndc project has undertaken similar work @xcite . notably, in these datasets, coreference information is also annotated. as a crucial first step for quote attribution, quotes need to be extracted from the main text. according to the findings by o'keefe et al., utilizing regular expressions for quote detection can achieve an accuracy exceeding 99% on clean english language data (o' @xcite . concerning quote attribution, multiple approaches have been explored in the existing literature. earlier works typically employed rule-based methods with grammatical rules, complemented by some machine learning techniques, which could be complex due to the need for defining extensive rules @xcite . in pan et al.'s novel understanding system in 2021, they treated speaker identification of dialogues (sid) as a gbdt-based ranking task. it involved first identifying characters using ner, then feeding input features of the candidate speaker and the target dialogue, such as position, distance, etc., into a model for classification to determine the final speaker from the candidates @xcite . to resolve coreferences, they split names into name clusters with primary and candidate names, extracted features like gender, overlapping, personal pronouns, etc., and used a gbdt-based model with name candidates for coreference resolution. lg pang employed bert and crf (conditional random field), framing the task as question answering, for speaker extraction of quotations 1 . yoder, michael miller, et al. and vishnubhotla, krishnapriya, et al. approached quotation attribution as a set of sequential sub-tasks: character identification, coreference resolution, quotation identification, and speaker attribution @xcite . in contrast to the recent work by @xcite we propose an additional module for classifying crowds, soliloquies, and dialogues. we omit the character identification module. instead, we employ a question-answering (qa) approach for quote attribution, which directly pre- ## dataset we utilize a dataset that we refer to as cld, comprising 1,991 modern chinese novels annotated by literature practitioners from the audiobook industry, ensuring high accuracy and validation. the original dataset includes information about the main characters and annotated novel texts, with each quote attributed to a specific character. for every character, we have access to their gender information and a brief description. ## quote attribution our primary objective is to correctly attribute each quote to the appropriate speaker within the novel. the process begins with quote extraction, where quotes are identified and extracted from the text. subsequently, we aim to assign the extracted quotes to their corresponding speakers accurately. we identify several key challenges in this task: ## results and discussion to evaluate the performance of these models, we randomly extract 10% of the dataset for evaluation for each model. here are our results. ## future work we recognize this work as a stepping stone towards a more comprehensive solution. here are some promising avenues for further exploration within the domain of automated quote attribution in literary works: these future directions have the potential to significantly improve the accuracy and efficiency of quote attribution in literary analysis. by continuously refining the methodology and exploring new avenues, we can pave the way for a fully automated quote attribution system that empowers researchers and enriches our understanding of literary works. ## conclusion this paper explored the application of machine learning for quote attribution in literary works. by leveraging ai-powered algorithms, we aim to empower literature annotators with faster and more accurate identification of quoted speech sources, ultimately enhancing analysis of fictional works. while writing styles may vary across novels, a significant portion of literary works can benefit from this approach.