Man vs. Machine: Extracting Character Networks from Human and Machine Translations (2022)

Abstract: Most of the work on Character Networks to date is limited to monolingual texts. Conversely, in this paper we apply and analyze Character Networks on both source texts (English novels) and their Finnish translations (both human- and machine-translated). We assume that this analysis could provide some insights on changes in translations that could modify the character networks, as well as the narrative. The results show that the character networks of translations differ from originals in case of long novels, and the differences may also vary depending on the novel and translator’s strategy.

## introduction character networks (cns) building can be considered as a part of social networks analysis (sna) research. the main difference between sna and cns extraction has to do with the type of datasets to which these methods are applied: cns extraction is typically used for different works of art (mainly literary texts of different genres as well as films), while sna is usually performed on more structured datasets, e.g. online social networks, such as youtube or livejournal @xcite . most of the work on cns to date applies them to monolingual texts. the main novelty of our work stems from the fact that we apply these techniques not only to original texts but also to their translations (both by human translators and by machine translation (mt) systems). in doing so, we aim to unveil whether the connections between characters (represented by cns), modified by human or machine translator, differ, which can point to narrative differences between original texts and translations. there are different tasks that could benefit from the extraction of cns in this bilingual setting, namely mt. mt could be enhanced with the contextual information, namely the global context of the whole text that could be in a form of a graph @xcite . we consider the task of cns extraction in the scope of enhancing mt of literary texts. in this framework, we consider the extraction of cns as a valuable first step, so that we can find out how the cns of human translation (ht) and mt compare to the cn of the original. in this paper we take a look at the cns of english originals and finnish translations thereof. the structure of the paper is as follows: first we provide an overview of the related work (section 2), subsequently we describe our data (section 3), after which we discuss the creation of the list of the characters' names that we will use for our methods (section 4). we continue the paper with describing the method (section 5) that we used. finally, we present our results of both qualitative analyses and quantitative assessment and analyze them (section 6). we conclude our paper in section 7. ## related work character networks extraction is a broad problem, so there have been many attempts to tackle it from different angles. it can be the main focus of the research @xcite , or only a step towards a broader goal, e.g. learning representations of stories based on character networks @xcite . it can be automated @xcite or not @xcite . the data for character networks extraction can also vary from movies @xcite to novels @xcite and fairytales @xcite . the most thorough overview of character network extraction so far has been done by @xcite . also @xcite , aside from their original topic of research, raised an issue regarding the evaluation of character networks: according to them, currently there is no standardized approach for such evaluation and most research on this topic evaluates the extracted networks by proxy or using sna metrics @xcite . the novelty of our research with respect to previous work lies mainly in three points: firstly, we are taking a look at cns of translations; secondly, we are looking at cns of machine-translated texts; and thirdly, we are looking at an uncommon language pair (english-finnish), since, as far as we know, there is no related work for character networks based on finnish texts to date. ## data the main dataset is made up of corpora of english and finnish literary texts. the english part of the dataset was gathered from project gutenberg ( https://www.gutenberg.org/ ), while the finnish human-translated subcorpus is available at the language bank of finland as the downloadable version of classics of english and american literature in finnish ( https://www. kielipankki.fi/corpora/ceal-2/ ). the english subcorpus contains two novels (pride and prejudice by jane austen, bleak house by charles dickens) and a short story (the washington square by henry james). the finnish human-translated corpus contains the corresponding finnish translations of these works carried out by kersti juva, while the finnish machine-translated subcorpus was created by deepl translator ( https://www.deepl. com/translator ) from the english originals on may 26, 2022. english and finnish human-translated subcorpora and english and finnish machine-translated subcorpora were sentence-aligned for consistency and for the purpose of doing close reading. the alignment was done using intertext @xcite . in case of human translations, the alignment was done semi-automatically (we had to go through the whole texts and align problematic sentences manually), but for mt it was done automatically, because the sentence splitting of the output translations of deepl corresponded to the one of the original texts. ## creation of character names' list before applying our methods (see section 5), we had to create a character names' list, so that we could use this list for implementation of our methods. to perform this task, we got the information from different internet sources that contain information about characters from the novels in our dataset (see appendix a). while creating a list of characters' names as a basis for our cns, we also faced many questions about characters, such as: what is a literary character? who do we consider a character from the point of the narrative? do we take into consideration off-screen characters (characters that are only mentioned in the text and do not participate in the plot)? to answer these questions, we needed to define what / who the character is. the literary character can be seen as a construct which definition and features depend on the study area @xcite . jannidis (2013) considered a character "a text-or media-based figure in a storyworld, usually human or human-like" or "an entity in a storyworld". overall, characters are interconnected with both narrative and storyworld and contribute to their development from many aspects. based on this notion, we considered a literary character every figure that was relevant for the narrative development (thus, e.g. names of famous persons that are mentioned but do not appear in the novel directly were not included). so we decided to include both onscreen (entities that are actively participating in the storyworld) and offscreen (entities that are passively contributing to the construction of the storyworld) characters (e.g. in case of washington square, it was the mother of the main character that was mentioned only twice, but never participated in the story herself). we also included all possible names that can be used for naming a certain character by splitting the full name (e.g. elizabeth bennet would also get versions elizabeth and bennet) and by analyzing possible versions (lizzy for elizabeth bennet) that were mentioned in the internet sources (see appendix a). we also included full names (if applicable) even if they were not used for naming a character in the text just for reference (e.g. in case of catherine sloper). so elizabeth bennet would get the following names: bennet, eliza, eliza bennet, elizabeth, elizabeth bennet, lizzy, and catherine sloper would get the names catherine, catherine sloper and sloper. the creation of the characters' names list was carried out only by one annotator. as a result, we would have a list of all possible characters' names. for this research we decided not to link different names of the same characters, because there were relatives and namesakes which were impossible to distinguish from the context. ## methods we extracted the character networks from english, finnish human-translated and finnish machinetranslated texts using the same workflow. the workflow was implemented using python with the help of the networkx library ( https://networkx. org/ ) for cn-related quantitative metrics and visualization. the workflow proceeds sequentially as follows: we decided to use chapters as the units to build the cns for several reasons. firstly, using smaller units (e.g. paragraphs) may have led to unexpected results, since the texts also contained dialogues and letters which may have zero characters in one paragraph despite having a clear link between the characters outside the dialogue or the letter. secondly, we consider a novel chapter as an autonomous part of narrative which may provide a more finalized view into characters' relationships. we consider our approach to be semi-automated, since we had to build lists of characters' names manually. we also decided to introduce some limitations to our research. for this paper, we decided not to link different versions of the names and their references, such as pronouns, due to the complexity of such a task, especially regarding finnish translations. for example, in pride and prejudice we would have 5 characters that could be linked to "miss bennet", namely all five bennet sisters: jane, elizabeth, mary, catherine and lydia. moreover, it is used in plural -there are "younger miss bennets" and "older miss bennets". as per our knowledge, there is also no coreference model for finnish language, and training such a model would require from us creation of the annotated corpus, which could be a topic for a paper on its own. for a similar reason we also decided not to use named entity recognition (ner) state-of-the-art tools, because our previous research has shown the need to further refine the results of ner pipeline: namely the results of lemmatization step for foreign names in finnish texts needed to be polished further @xcite . ## evaluation and results after implementing our workflow, we used it on our corpus, producing cns for english original texts, finnish human translations and finnish mt outputs. for our assessment, we performed both qualitative and ative analyses. quantitative analysis was done by analysing the cns' metrics and qualitative analysis was done to provide some insights and possible reasons for such results of quantitative analysis. ## conclusion we have created character networks for original texts, for human translations and machine translations for three novels. results show that for longer novels there are changes in character networks both in human and machine which may be attributed to the translator style or the target language features in human translations and to the models used in machine translations. one of the most interesting results is that the main 5 characters of pride and prejudice change in human translations with jane bennet replacing mr. wickham. we assume that our research could be enhanced further e.g. by using coreference which would require the creation of an annotated corpus, by grouping different versions of character names together (either manually or automatically) and by studying differ-ent language pairs as source-target languages for originals and translations.