Papers by Edoardo Manino

2 papers
Montague semantics and modifier consistency measurement in neural language models (2025.coling-main)

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Challenge: Existing studies on distributional language models have been focused on linguistics and their relationship with semantic formalisms for decades.
Approach: They propose a method for measuring compositional behavior in contemporary language embedding models by introducing three new tests inspired by Montague semantics.
Outcome: The proposed method measures compositional behavior in language embedding models on adjectival modifier phenomena in adjective-noun phrases.
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective (2022.findings-acl)

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Challenge: Existing studies focus on robustness-like metamorphic relations, which limit the scope of linguistic properties they can test.
Approach: They propose three new classes of metamorphic relations which address the properties of systematicity, compositionality and transitivity.
Outcome: The proposed methods show that metamorphic models do not always behave according to expected linguistic properties.

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