DOI: 10.5281/zenodo.17365381
title: SIT-Model

description: Syncretic Interpretive Transformer (SIT)

Overview

Syncretic Interpretive Transformer (SIT) is an advanced deep semantic modeling framework designed to analyze and interpret Chinese and Western cultural classics.It integrates multimodal encoding, tradition-specific interpretive layers, and a Cross-Civilizational Attention Module (CCAM), enhanced with a Dialectical Alignment Protocol (DAP) to align and contrast philosophical texts across civilizations while preserving their unique cultural logic.

SIT is particularly effective for studying Confucian, Taoist, and Buddhist classics as well as ancient Greek and Roman philosophical texts and Renaissance literature. It provides a foundation for cross-cultural semantic computing and digital humanities research.

✨ Features

Multimodal Symbolic Modeling



Integrates textual and image features (e.g., classical book scans, annotated versions).

Employs a Spiking Layer + Frequency-Aware Mixer (see Fig. 1, p.7) to improve fine-grained semantic representation.


Tradition-Sensitive Interpretation



Two separate interpretive pathways:



Chinese tradition: metaphor–analogy–resonance reasoning.

Western tradition: deductive–axiomatic–structural reasoning.



Cross-civilizational semantic alignment is performed through CCAM.


Dialectical Alignment Protocol (DAP)



Captures cultural–semantic tension dynamically.

Treats divergence as interpretive richness rather than noise.

See Fig. 3 (p.10) for the structural diagram.


Cross-Cultural Contrastive Learning



Uses contrastive objectives to align classical texts across traditions.

Integrates knowledge graphs with transformer representations for accurate cross-cultural semantic mapping.


📊 Datasets




Dataset
Content
Key Features




Chinese Cultural Texts Semantic Dataset
Confucian, Taoist, Buddhist classics, poetry, histories
Rich in metaphors and cultural imagery


Western Literary Classics Dataset
Plato, Aristotle, Shakespeare, and others
Includes original and modern translations


Cross Cultural Semantic Analysis Dataset
Aligned Chinese-Western pairs
Annotated for themes, metaphors, value frames


Multilingual Classics Classification Dataset
10+ languages
Supports zero-shot and transfer learning




Full dataset description is available on pages 12–13, including annotation schema and multilingual labeling strategy.

⚙️ Installation



Clone the repository
git clone https://zenodo.org/records/17365382


🚀 Usage



Latent semantic vectors

Dialectical Divergence Score (philosophical tension)

Concept cluster labels and alignment mapping


🧪 Applications



Cross-civilization philosophical thought analysis

Symbolism and metaphor detection across traditions

Digital humanities and cultural computing research

Interpretability tools (attention heatmaps, divergence graphs)


🧩 Model Components



Syncretic Interpretive Transformer (SIT) — dual-path interpretive architecture

Cross-Civilizational Attention Module (CCAM) — semantic space alignment

Epistemic Modulator — multi-layer interpretive weighting

Dialectical Alignment Protocol (DAP) — cultural–semantic tension modeling

Fusion Module — multimodal feature alignment and aggregation


📈 Performance




Dataset
Accuracy
F1 Score
AUC




Chinese Cultural
92.76
90.91
94.02


Western Literary
91.65
89.93
93.10


Cross-Cultural
91.48
89.41
93.11


Multilingual Classics
90.62
88.67
92.38




Experimental results are reported on pages 14–16, Tables 1–2.

🧭 Future Work



Extend support to Arabic, Sanskrit, and Hebrew classics

Enhance adaptive symbolic encoding to reduce reliance on predefined vocabularies

Develop interactive visualization interface for philosophical tension mapping

Expand cross-domain transfer learning to contemporary thought texts


📜 License

This project is licensed under the MIT License.

🙏 Acknowledgments

This research is supported by the Education Department of Shaanxi Province (Project No. 21JK0108).Special thanks to the cross-cultural semantic computing and digital humanities research community.Author: Xinxin Wang, Shangluo University.