Academic

Automated Motif Indexing on the Arabian Nights

arXiv:2603.19283v1 Announce Type: new Abstract: Motifs are non-commonplace, recurring narrative elements, often found originally in folk stories. In addition to being of interest to folklorists, motifs appear as metaphoric devices in modern news, literature, propaganda, and other cultural texts. Finding expressions of motifs in the original folkloristic text is useful for both folkloristic analysis (motif indexing) as well as for understanding the modern usage of motifs (motif detection and interpretation). Prior work has primarily shown how difficult these problems are to tackle using automated techniques. We present the first computational approach to motif indexing. Our choice of data is a key enabler: we use a large, widely available text (the Arabian Nights) paired with a detailed motif index (by El-Shamy in 2006), which overcomes the common problem of inaccessibility of texts referred to by the index. We created a manually annotated corpus that identified 2,670 motif expressions

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Ibrahim H. Alyami, Mark A. Finlayson · · 1 min read · 8 views
#cs.CL

arXiv:2603.19283v1 Announce Type: new Abstract: Motifs are non-commonplace, recurring narrative elements, often found originally in folk stories. In addition to being of interest to folklorists, motifs appear as metaphoric devices in modern news, literature, propaganda, and other cultural texts. Finding expressions of motifs in the original folkloristic text is useful for both folkloristic analysis (motif indexing) as well as for understanding the modern usage of motifs (motif detection and interpretation). Prior work has primarily shown how difficult these problems are to tackle using automated techniques. We present the first computational approach to motif indexing. Our choice of data is a key enabler: we use a large, widely available text (the Arabian Nights) paired with a detailed motif index (by El-Shamy in 2006), which overcomes the common problem of inaccessibility of texts referred to by the index. We created a manually annotated corpus that identified 2,670 motif expressions of 200 different motifs across 58,450 sentences for training and testing. We tested five types of approaches for detecting motif expressions given a motif index entry: (1) classic retrieve and re-rank using keywords and a fine-tuned cross-encoder; (2) off-the-shelf embedding models; (3) fine-tuned embedding models; (4) generative prompting of off-the-shelf LLMs in N-shot setups; and (5) the same generative approaches on LLMs fine-tuned with LoRA. Our best performing system is a fine-tuned Llama3 model which achieves an overall performance of 0.85 F1.

Executive Summary

This article presents the first computational approach to motif indexing, a task critical to folkloristic analysis and modern motif detection. The authors use a large, annotated text of the Arabian Nights paired with a detailed motif index to train and test five types of approaches for detecting motif expressions. The best performing system is a fine-tuned Llama3 model achieving 0.85 F1. While the results are promising, the study's limitations, such as its reliance on a single dataset and evaluation metric, highlight the need for further research in this area. The findings have implications for both folkloristic analysis and the development of more sophisticated natural language processing tools.

Key Points

  • ▸ The article presents a novel computational approach to motif indexing.
  • ▸ The authors use a large, annotated text of the Arabian Nights and a detailed motif index.
  • ▸ The best performing system is a fine-tuned Llama3 model achieving 0.85 F1.

Merits

Strength

The study presents a novel and significant contribution to the field by developing a computational approach to motif indexing.

Strength

The use of a large, annotated text and a detailed motif index provides a robust evaluation framework for the proposed approaches.

Strength

The fine-tuned Llama3 model achieves competitive performance, highlighting the potential of large language models for complex tasks.

Demerits

Limitation

The study relies on a single dataset, the Arabian Nights, which may not generalize well to other folkloristic texts.

Limitation

The evaluation metric used, F1 score, may not capture the nuances of motif indexing tasks, which often require more subtle judgments.

Limitation

The study does not explore the interpretability of the proposed approaches, which is a critical aspect of motif indexing.

Expert Commentary

The study presents a significant contribution to the field of motif indexing, which is critical for both folkloristic analysis and the development of more sophisticated NLP tools. While the results are promising, the study's limitations highlight the need for further research in this area. The use of large language models and fine-tuning techniques is a promising direction for future work, and the study's findings have implications for both practical applications and policy decisions.

Recommendations

  • ✓ Future studies should explore the generalizability of the proposed approaches to other folkloristic texts and datasets.
  • ✓ The development of more interpretable and transparent NLP models is essential for motif indexing tasks, which often require subtle judgments.

Sources

Original: arXiv - cs.CL

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