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Exploring Detection of Complex, Non-Literal Expressions of Cultural Motifs

Motifs are non-commonplace, recurring narrative elements, often found originally in folk stories and also in modern news, literature, and propaganda. Expressions of motifs in text can be most straightforwardly classified as simple or complex. Simple motif expressions are easy to detect because they almost always appear in a single sentence using the same words as the motif definition itself. However, complex motifs are strongly non-literal and often spread across multiple sentences, thus requiring more context to understand. We propose a baseline system to detect complex motif expressions that have challenged prior work. We used an annotated corpus that identified 992 complex motif expressions of 155 different motifs for training and testing. We tested five different generative approaches that included varying amounts of context: a single sentence baseline (from prior work); a window of 3 or 5 sentences; the entire story; or the entire story with the target sentence identified. We fine-tuned four off-the-shelf open-source LLMs using LoRA under these conditions. Somewhat surprisingly, we report a negative result: our experiments show that in our generative setup more context does not improve the detection of complex motifs. We speculate on why this might be so and identify directions for future research.