CascadeNS: Confidence-Cascaded Neurosymbolic Model for Sarcasm Detection

DOI

10.2991/978-94-6239-707-1_6How to use a DOI?

Keywords

Sarcasm Detection; Neurosymbolic AI; Semigraph Representation; Natural Language Processing

Abstract

Sarcasm detection in product reviews requires balancing domain-specific symbolic pattern recognition with deep semantic understanding. Symbolic representations capture explicit linguistic phenomena that are often decisive for sarcasm detection. Existing work either favors interpretable symbolic representation or semantic neural modeling, but rarely achieves both effectively. Prior hybrid methods typically combine these paradigms through feature fusion or ensembling, which can degrade performance. We propose CascadeNS, a confidencecalibrated neurosymbolic architecture that integrates symbolic and neural reasoning through selective activation rather than fusion. A symbolic semigraph handles pattern-rich instances with high confidence, while semantically ambiguous cases are delegated to a neural module based on pre-trained LLM embeddings. At the core of CascadeNS is a calibrated confidence measure derived from polarityweighted semigraph scores. This measure reliably determines when symbolic reasoning is sufficient and when neural analysis is needed. Experiments on product reviews show that CascadeNS outperforms strong baselines by 7.44%.

Code: GitHub repository

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Swapnil Mane
AU  - Vaibhav Khatavkar
PY  - 2026
DA  - 2026/06/18
TI  - CascadeNS: Confidence-Cascaded Neurosymbolic Model for Sarcasm Detection
BT  - Proceedings of the International Conference on Recent Advances in Intelligent and Sustainable Technologies (RAIST 2026)
PB  - Atlantis Press
SP  - 59
EP  - 69
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-707-1_6
DO  - 10.2991/978-94-6239-707-1_6
ID  - Mane2026
ER  -