Papers by Marcos Garcia
Probing for idiomaticity in vector space models (2021.eacl-main)
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| Challenge: | Contextualised word representation models are used to represent idiomaticity in language. |
| Approach: | They propose probing measures to assess if some of the expected linguistic properties of noun compounds are readily available in some standard and widely used representations. |
| Outcome: | The proposed models show that idiomaticity is not yet accurately represented by contextualised models. |
Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy (2021.acl-long)
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| Challenge: | Existing models that represent different senses of words in context are not accurate for polysemous words. |
| Approach: | They propose a multilingual dataset that evaluates the ability of models to accurately represent different lexical-semantic relations such as homonymy and synonymy. |
| Outcome: | The proposed models can disambiguate homonyms in context, but fail to represent words with different senses when occurring in similar sentences. |
False Friends or Cognates? A Cross-lingual Semantic Ambiguity Evaluation for Galician, Portuguese and Spanish (2026.acl-long)
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| Challenge: | Closely related languages exhibit a high degree of lexical and orthographic similarity, which can facilitate cross-lingual understanding but also give rise to systematic semantic ambiguity. |
| Approach: | They introduce six cross-lingual datasets that are manually or semi-automatically generated and are able to identify and process false friends among these languages. |
| Outcome: | The proposed models can identify and process false friends among Galician, Portuguese, and Spanish. |
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels (2021.acl-long)
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| Challenge: | Existing resources for idiomaticity annotation only include ratings at type level . idioms such as noun compounds have been considered a challenge for NLP . |
| Approach: | They present a dataset with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token levels. |
| Outcome: | The proposed dataset shows that human annotations are not capturing idiomaticity as human annotation models. |
The iRead4Skills Intelligent Complexity Analyzer (2025.emnlp-demos)
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Wafa Aissa, Raquel Amaro, David Antunes, Thibault Bañeras-Roux, Jorge Baptista, Alejandro Catala, Luís Correia, Thomas François, Marcos Garcia, Mario Izquierdo-Álvarez, Nuno Mamede, Vasco Martins, Miguel Neves, Eugénio Ribeiro, Sandra Rodriguez Rey, Elodie Vanzeveren
| Challenge: | 20% of EU adult population exhibits low-literacy and numeracy skills (EA, 2021). |
| Approach: | iRead4Skills Intelligent Complexity Analyzer integrates a range of NLP components to assess input texts along multiple levels of granularity and linguistic dimensions in Portuguese, Spanish, and French. |
| Outcome: | The system assigns four tailored difficulty levels and introduces four diagnostic yardsticks—textual structure, lexicon, syntax, and semantics—offering users actionable feedback on specific dimensions of textual complexity. |
Pay Attention when you Pay the Bills. A Multilingual Corpus with Dependency-based and Semantic Annotation of Collocations. (P19-1)
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| Challenge: | resulting corpus can be useful for different NLP tasks such as natural language understanding or natural language generation. |
| Approach: | They propose to annotate 155k tokens and 1,526 collocations in context in a multilingual corpus in English, Portuguese, and Spanish. |
| Outcome: | The new corpus can be used to evaluate different approaches for collocation identification, which can be useful for different NLP tasks such as natural language understanding or natural language generation. |
Dependency resolution at the syntax-semantics interface: psycholinguistic and computational insights on control dependencies (2023.acl-long)
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| Challenge: | Using psycholinguistic and computational experiments, we compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences. |
| Approach: | They compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences such as ‘José le prometió/ordenó a Mara ser ordenado/a’. |
| Outcome: | The models fail to identify the correct antecedent in non-adjacent dependencies, showing their reliance on linearity. |