Computer-aided Analysis of Unreliability and Truth in Fiction - Interconnecting and Operationalizing Narratology (CAUTION)

• Applicants: Dr. Janina Jacke; Professor Dr. Jonas Kuhn
• Subject Area: German Literary and Cultural Studies (Modern German Literature) ; General and Comparative Linguistics, Experimental Linguistics, Typology, Non-European Languages
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 449444411

The CAUTION project aims to strengthen interconnections across subfields of literary studies, (computational) linguistics and digital humanities (DH), addressing the aesthetic phenomenon of unreliable narration. A. Schnitzler's novella 'Andreas Thameyers letzter Brief' (1902) is a typical example. The narrator tries to convince addressees that his wife has been faithful, but to a commonsensical reader the 'evidence' he provides tells quite a different story. She gave birth to a dark-skinned baby nine month after being alone with foreigners from an exotic country. The phenomenon presents challenges to approaches of text description/interpretation that emphasize text-based operationalization of concepts. The research literature abounds with discussions of interpretive implications for concrete examples of unreliable narration - but can text-independent guidelines be devised that lead to an intersubjectively stable assessment of plausible readings by trained literary scholars? In this respect, unreliable narration is paradigmatic for phenomena whose key characteristics can only be captured at a 'depth' of analysis exceeding the level of literal semantic interpretation. Documenting the processes of (re)constructing non-literal meanings is notoriously difficult as it involves long inference chains and implicit or background knowledge.Most DH work on literary texts has so far addressed aspects associated with surface properties (e.g. style, topic structure, or networks of textually evident cross-character relations): Recent Natural Language Processing methods require large amounts of data, which are easier to obtain for surface-level phenomena. Moreover, annotated datasets for systematic narratological analysis are only beginning to become available. Hence it is currently hard to assess whether the generalizing potential, e.g., in neural modeling architectures, could capture aspects of the process of assigning non-literal meanings to texts: Theoretically grounded reference data for testing are missing.Unreliable narration is an apt case for experimenting with the annotation of derived meanings and thus filling the gap. Attributing unreliability to a narrator involves inferences beyond plain linguistic interpretation, but the inference chains leading different readers of the same text to this conclusion have a similar logical structure. Our collaborative text annotation will target this parallelism in reasoning about the narrator. We formalize interpretive strategies a reader can adopt using a multi-agent belief revision system from AI. Working on a corpus of systematically chosen texts, we will work cyclically towards a catalogue of formalizations for annotators to choose from to capture their reading of a text. Since inference chains for non-trivial stories are complicated, it is only through computational inference engines that the alternative formulations can be checked and improved - aiming to suitably capture patterns in the entire corpus.

• DFG Programme: Research Grants