Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts

Problem
The paper addresses the challenge of zero-shot irony detection in social media texts, a task that remains difficult for Large Language Models (LLMs) due to their tendency to interpret language literally. The authors highlight a gap in existing literature regarding the effectiveness of hybrid neuro-symbolic approaches in this context. Notably, this work is a preprint and has not undergone peer review.

Method
The authors propose the Robust Dual-Signal (RDS) Fusion framework, which integrates a hybrid neuro-symbolic architecture with a compressed Chain-of-Thought (CoT) reasoning mechanism. The architecture operates without Supervised Fine-Tuning (SFT), relying instead on a frozen CoT pipeline to enhance performance. The model processes input through three signals: a neural baseline, a symbolic prior, and the CoT pipeline. The training methodology and compute resources are not explicitly detailed, but the evaluation is conducted on two datasets: the TweetEval test set and the iSarcasm dataset.

Results
On the TweetEval test set (N=734), the RDS framework achieves an accuracy of 78.1% and a Macro F1 score of 0.777, matching the performance of a fine-tuned BERTweet model. In contrast, on the imbalanced iSarcasm dataset, the RDS framework demonstrates a zero-shot Macro F1 score of 0.6726 and an Ironic F1 score of 0.4821, outperforming several heavily supervised transformer ensembles from the SemEval competition. Statistical ablation studies reveal that the addition of the symbolic prior to the neural baseline does not yield significant improvements (p = 0.242), while the marginal benefit of incorporating the CoT pipeline is also minimal (p = 0.149). Only the complete fusion of all three signals results in a statistically significant enhancement over the baseline (p = 0.005).

Limitations
The authors acknowledge that their approach may not generalize well to other forms of irony or sarcasm beyond the datasets used. Additionally, the reliance on a frozen CoT pipeline may limit adaptability to dynamic language use in social media. The paper does not explore the computational efficiency of the proposed framework, which could be a concern for real-time applications.

Why it matters
This work has significant implications for the development of irony detection systems in natural language processing, particularly in social media contexts where nuanced language is prevalent. The successful integration of neuro-symbolic methods could pave the way for more robust models that require less supervision, thus reducing the dependency on large labeled datasets. The findings contribute to ongoing discussions in the field regarding the efficacy of hybrid approaches in addressing complex linguistic phenomena, as published in arXiv cs.CL.