## 3 Materials and Methods  
### Validity of Using Character Utterances  
Before developing our language model for utterance translation, we investigated whether the use of utterances really reflects the personality of characters. That is, although the previous studies have already found that linguistic style in novels has a strong relationship with personality [16], we wanted to answer the question \"Do the characters who are similar in terms of personality really talk in a similar way?\" because the characters are not real people, but only fictional characters. Based on the Big Five traits, we first annotated the OCEAN scores of 28 novels from the Project Dialogism Novel Corpus (PDNC) [33]. PDNC was chosen for the data because it is a structured corpus of character utterances extracted from a variety of classic English and American novels, and because it is manually identified and annotated by experts in Western literature. Specifically, to control for factors such as genre and historical context, we used the PDNC dataset, which focuses on 19 th- and early 20 th-century English and American novels. It primarily includes works by authors like [PERSON], [PERSON], and [PERSON], whose writings center on domestic life and individual relationships, enabling experiments on a relatively cohesive and thematically consistent set of classic literary texts. The 28 novels include _A Handful of Dust, A Passage to India, A Room with a View, Alice's Adventures in Wonderland, Anne of Green Gables, Daisy Miller, Emma, Hard Times, Howards End, Mansfield Park, Night and Day, Northanger Abby, Oliver Twist, Persuasion, Pride and Prejudice, Sense and Sensibility, The Age of Innocence, The Awakening, The Gambler, The Invisible Man, The Man Who Was Thursday, The Mysterious Affair at Styles, The Picture of Dorian Gray, The Sign of the Four, The Sport of the Gods, The Sun Also Rises, Where Angels Fear to Tread and Winnie-the-Pool._  
Each character in the novels is annotated with five scores ranging from \(-3\) to \(3\), each corresponding to one of the five traits O, C, E, A, and N. The annotation was performed by five graduate assistants trained in literary studies. Each character was annotated by two annotators. Personality can be assessed through either self-report or report-by-others methods. Since the process of constructing a personality annotated dataset is inherently challenging, a recent study that released a new dataset for over 1000 individuals also adopted the report-by-others method for personality annotation [34]. We adopted the report-by-others method in this study, as the subjects of personality measurement are fictional rather than real individuals. We filtered characters that did not have enough utterances and obtained 139 characters. Pearson correlation coefficients are 0.51, 0.43, 0.30, 0.51, and 0.36, for O, C, E, A, and N.  
According to [35], these coefficients are regarded as medium or large. We also computed Intraclass Correlation Coefficient (ICC) to assess the reliability of the scores annotated by the annotators for O, C, E, A, and N. ICC(3,1) [36], which measures the consistency of ratings for a single measurement, yielded the following values in our study: O = 0.50, C = 0.42, E = 0.31, A = 0.51, and N = 0.35. These generally showed fair levels of agreement (0.42) [37] with lower consistency observed for E and N. On the other hand, ICC(3,2), which assesses the reliability of the average of two raters, showed generally higher values in our study: O = 0.66, C = 0.59, E = 0.47, A = 0.67, and N = 0.52. These results suggest that the personality trait scores used in this paper provide a reasonable level of reliability. Examples of the annotated characters are shown in Table 2. The OCEAN distributions are shown in Figure 2.  
Based on the annotated data, we created two different clusters: personality-based clusters and word-based clusters.