Papers by João António Rodrigues

4 papers
Universal Grammatical Dependencies for Portuguese with CINTIL Data, LX Processing and CLARIN support (2022.lrec-1)

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Challenge: a new collection of quality language resources is presented for the computational processing of the Portuguese language . the framework for the mapping between linguistic form and meaning is centered on the notion of grammatical relation .
Approach: They propose a new set of quality language resources for the computational processing of the Portuguese language under the Universal Dependencies framework.
Outcome: The proposed framework provides for the mapping between linguistic form and meaning representations.
Reproduction and Revival of the Argument Reasoning Comprehension Task (2020.lrec-1)

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Challenge: Reproduction of scientific results is essential for scientific development across all disciplines.
Approach: They evaluate scientific reproduction of arguments reasoning comprehension systems . they find reproducing results of previous work is a basic requirement for validating hypothesis .
Outcome: The proposed systems were compared with the revised data set and scored in line with the results of the argument reasoning comprehension task.
Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning (2021.emnlp-main)

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Challenge: a recent study has found that commonsense reasoning models are learning transferable generalizations . commonsensibility is a human capacity that has been a core challenge to Artificial Intelligence since its inception.
Approach: They conduct an analysis of benchmarks that involve commonsense reasoning . they find that most datasets experimented with are problematic . commonsensence is a quintessential human capacity .
Outcome: The proposed model is able to perform well on commonsense reasoning tasks . the model is not learning transferable generalizations or taking advantage of shortcuts .
Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness (2020.coling-main)

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Challenge: lexical semantics theories advocate that the meaning of words is represented as an inference graph, a feature mapping or a cooccurrence vector.
Approach: They propose to probe different lexical semantics theories for their levels of cognitive plausibility and technological usefulness.
Outcome: The proposed approach is superior to the other approaches, the authors argue . they show that the feature-based approach is more useful and more plausible than the other ones .

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