Papers by Jakub Dotlacil
The Learnability of Model-Theoretic Interpretation Functions in Artificial Neural Networks (2026.findings-acl)
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| Challenge: | Entity vectors improve scores on basic event, while gated architectures benefit most. |
| Approach: | They extend entity-level semantic representations, modern architectures, principled competing event generation, extended systematicity tests and a two-dimensional difficulty analysis disaggregating results by modifier complexity. |
| Outcome: | The proposed model-theoretic interpretation functions generalize systematically to out-of-training-sample sentences. |