In this paper, we present a post-hoc method to produce highly interpretable global rules to explain NLP classifiers. Rules are extracted with a data mining approach on a semantically enriched input representation, instead of using words/wordpieces solely. Semantic information yields more abstract and general rules that are both more explanatory and less complex, while being also better at reflecting the model behaviour.
Zugarini, A., Rigutini, L. (2023). SAGE: Semantic-Aware Global Explanations for Named Entity Recognition. In 2023 International Joint Conference on Neural Networks (IJCNN) Proceedings (pp.1-8). New York : IEEE [10.1109/IJCNN54540.2023.10191364].
SAGE: Semantic-Aware Global Explanations for Named Entity Recognition
Rigutini, Leonardo
Supervision
2023-01-01
Abstract
In this paper, we present a post-hoc method to produce highly interpretable global rules to explain NLP classifiers. Rules are extracted with a data mining approach on a semantically enriched input representation, instead of using words/wordpieces solely. Semantic information yields more abstract and general rules that are both more explanatory and less complex, while being also better at reflecting the model behaviour.
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https://hdl.handle.net/11365/1245814