Improving Quotation Attribution with Fictional Character Embeddings (2024)

Abstract: Humans naturally attribute utterances of direct speech to their speaker in literary works.When attributing quotes, we process contextual information but also access mental representations of characters that we build and revise throughout the narrative. Recent methods to automatically attribute such utterances have explored simulating human logic with deterministic rules or learning new implicit rules with neural networks when processing contextual information.However, these systems inherently lack character representations, which often leads to errors in more challenging examples of attribution: anaphoric and implicit quotes.In this work, we propose to augment a popular quotation attribution system, BookNLP, with character embeddings that encode global stylistic information of characters derived from an off-the-shelf stylometric model, Universal Authorship Representation (UAR).We create DramaCV, a corpus of English drama plays from the 15th to 20th century that we automatically annotate for Authorship Verification of fictional characters utterances, and release two versions of UAR trained on DramaCV, that are tailored for literary characters analysis.Then, through an extensive evaluation on 28 novels, we show that combining BookNLP’s contextual information with our proposed global character embeddings improves the identification of speakers for anaphoric and implicit quotes, reaching state-of-the-art performance.Code and data can be found at https://github.com/deezer/character_embeddings_qa.

## introduction authors of literary works employ various devices to create engaging narratives, often combining narration with character dialogues to unveil the plot. fictional characters portray themselves through dialogues, revealing aspects of their personality, their own style and ideas about themselves and the fictional world. as part of studying characters in digi-"as soon as ever mr. bingley comes, my dear," said mrs. bennet, "you will wait on him of course." "no, no. you forced me into visiting him last year, and promised if i went to see him, he should marry one of my daughters..." his wife represented to him how absolutely necessary such an attention would be from all the neighbouring gentlemen, on his returning to netherfield. recent approaches to quotation attribution typically succeed at attributing explicit quotes but struggle when it comes to anaphoric and implicit quotes @xcite @xcite . explicit utterances occur when the narrator indicates the speaker of a quote with a speech verb and a named mention, while anaphoric quotes are introduced with a speech verb and a pronoun or common noun. when no narrative information is given about the speaker of the quote, we refer to those as implicit quotes. examples of such quotes are given in figure 1 . attributing anaphoric and implicit quotes usually requires fine-grained understanding of contextual information such as discerning discussion patterns and long-range information such as linking pronouns to their canonical entity. accessing information of fictional characters (style, persona, gender) also helps disambiguate challenging examples of attribution. variations of writing style and long-range contextual dependencies, ubiquitous in fictional works, add an extra layer of complexity, making attribution of non-explicit quotes a complex task to solve even with recent tools. @xcite some of these shortcomings have been addressed by improving link prediction between pronouns/common nouns and canonical characters @xcite or by exploiting better contextual representations @xcite . instead, we show that combining fictional character representations encoding global stylistic information and topical preferences with contextual information processed by booknlp, foot_1 , a widely used nlp pipeline designed for literary texts, improves the speaker attribution of anaphoric and implicit quotes. as explicit utterances are often accurately attributed to their speaker @xcite , we assume those quotes given, and use them to derive stylistic information of characters. among methods to represent characters from the words they utter, previous works @xcite @xcite have explored embeddings encoding topical preferences using sbert @xcite or stylistic information using universal authorship representations (uar) @xcite , a fine-tuned variant of sbert trained on the authorship verification (av) task with millions of reddit users. uar's stylometric embeddings built from character utterances were previously shown to distinguish fictional characters among the same work @xcite but considering the discrepancies between reddit and literary discourse, we question the effectiveness of these embeddings to represent characters. to overcome this potential limitation, we build variants of uar tailored for literary texts that we validate on the av task with drama and novel characters. stylometric character representations computed with our uar variants significantly improve av of characters in literary works, while also reaching state-of-the-art quotation attribution accuracy of non-explicit quotes when combined with booknlp contextual representations. to sum up, our contributions are: ## representing fictional characters in this section, we present our approach to build and evaluate character representations tailored for literary texts, focusing on aspects of style and topical preferences of characters. to build these representations, we train variants of uar from scratch on the authorship verification (av) of characters in the literary domain, considering a fictional character as an author in the av terminology. the av of fictional characters aims at answering the following problem: given two collections of quotes that have been uttered by characters within the same fictional work, predict whether they were spoken by the same character or not. although closely linked to authorship attribution, we note that this framework is different for the following reasons: 1) we do not have access to a list of canonical characters for every literary work and 2) we compare characters within the same story, implying that each quote is inherently influenced by the author's style. uar adapts sbert by directly encoding collections of documents, and is trained to distinguish if two collections of documents have been written by the same author or not. although the reddit version of uar appears to perform well on pdnc @xcite , which is on-par with its zeroshot domain transfer abilities, we question its suitability for literary analysis and propose to train instead a domain-specific variant of uar tailored for literary texts. only a few datasets containing annotations of the speaker of an utterance of direct speech in full novels exist, as annotating entire works is costly. instead, prior works have considered using movie a segment in the scene split is a single scene or act in a play, while a segment in the play split is the entire play. for each segment, the targets are collections of utterances uttered by all participating characters in the segment. the explicit split of pdnc @xcite that we use to test generalization is also displayed for comparison. @xcite @xcite or drama scripts @xcite as they come naturally annotated with the speaker of each utterance. we follow this approach and propose dramacv (drama character verification), a dataset of english drama plays focused on the av of fictional characters. we chose to use drama plays as they are mostly similar to novels in terms of writing conventions, and because we were able to extract a number of characters and utterances an order of magnitude above the available datasets of annotated novels. the rest of this section evaluates our variants of uar trained on dramacv on the test set of dramacv and on fictional characters participating in the 28 pdnc novels. ## quotation attribution motivated by the ability of uar to encode character utterances in a representation space that contains useful, though insufficient, information for quotation attribution, we evaluate in the next section the impact of combining these global character embeddings with booknlp's contextual information. booknlp is a natural language processing pipeline dedicated to english books, popular among practitioners of digital humanities. ## conclusion in this work, we evaluated fictional character embeddings constructed with the explicit quotes they utter, showing that they improve quotation attribution accuracy of non-explicit quotes on pdnc when used in combination with booknlp's contex- we found that booknlp's attribution accuracy is not distributed uniformly across novels, mainly caused by noisy coreference resolution, resulting in many quotes not having contextual candidate mentions that refer to their true speaker. we believe that including our character embeddings in the coreference resolution model as in @xcite might also improve its performance and thus increase overall attribution accuracy. another potential solution could include unmasking the closest quotes in the vicinity of a target quote that are likely to contain addressee information. we leave better combination of fictional character embeddings with booknlp for future exploration.