Marco Idiart — accepted AI-conference papers

2024

Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss

Wei He, Marco Idiart, Carolina Scarton, Aline Villavicencio

ACL 2024findings

Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in Natural Language Processing (NLP). This is partly because these expressions do not derive their meanings solely from their constituent words, but also due to the scarcity of relevant data resources, and…

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2021

Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels

Marcos Garcia, Tiago Kramer Vieira, Carolina Scarton, Marco Idiart, Aline Villavicencio

ACL 2021long

Accurate assessment of the ability of embedding models to capture idiomaticity may require evaluation at token rather than type level, to account for degrees of idiomaticity and possible ambiguity between literal and idiomatic usages. However, most existing resources with annotation of idiomaticity…

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