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.
- Seventeen features combine entity relations, polarity matching, and embedding alignment.
- Logistic regression scores 83% on SICK and 96% on SICK-CE.
- Within two points of RoBERTa at a fraction of the compute.
- SHAP finds structural features primary and distributional features complementary.
From the original article
1 min read at arxiv.orgDespite the success of neural models in natural language processing, their black-box nature limits interpretability and conceals the linguistic phenomena underlying their predictions.
By David Torres-Moreno · arxiv.org
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