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Explainable NLP techniques for complex cognitive tasks

Develop explainable Neuro-Symbolic natural language processing techniques capable of handling complex cognitive tasks while providing interpretable rationales aligned with logical structures.

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Background

The review covers advances in discourse-aware summarization, confounder correction, humor understanding, and factuality-aware training. The authors note that building explainable NLP techniques for complex cognitive tasks remains an open question.

References

Open research questions remain around how Neuro-Symbolic AI can adapt and evolve symbolic representations in real-time to maintain transparency, integrate meta-cognitive mechanisms for self-monitoring and adjustment of reasoning strategies, develop explainable NLP techniques for complex cognitive tasks, and ensure factual consistency in AI outputs while providing clear, detailed explanations of the underlying reasoning process.

Neuro-Symbolic AI in 2024: A Systematic Review (2501.05435 - Colelough et al., 9 Jan 2025) in Section 4.3 Explainability and Trustworthiness