Linguistic Features for Interpretable Textual Entailment
SLITE uses 17 linguistic features for textual entailment, reaching 83% on SICK and nearing RoBERTa.
The paper introduces SLITE, a hybrid explainable model for recognizing textual entailment that pairs structural-relational semantic features with distributional measures including entropy and transfer entropy. Logistic regression on 17 features reaches 83% accuracy on three-class SICK and 96% on SICK-CE, outperforming IsoLex by 4 points and landing within 2 points of RoBERTa at much lower cost. Ablation and SHAP analysis show structural-relational features drive most decisions, while distributional features help detect neutrality and contradiction.
24