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SemEval-2022 Task

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dc.contributor.author Zamparelli, Roberto
dc.contributor.author Chowdhury, Shammur A
dc.contributor.author Brunato, Dominique
dc.contributor.author Chesi, Cristiano
dc.contributor.author Dell’Orletta, Felice
dc.contributor.author Hasan, Arid
dc.contributor.author Venturi, Giulia
dc.date.accessioned 2024-03-19T06:56:09Z
dc.date.available 2024-03-19T06:56:09Z
dc.date.issued 2022-07-20
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11719
dc.description.abstract We report the results of the SemEval 2022 Task 3, PreTENS, on evaluation the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relation. The task featured two sub-tasks articulated as: (i) binary prediction task and (ii) regression task, predicting the acceptability in a continuous scale. The sentences were artificially generated in three languages (English, Italian and French). 21 systems, with 8 system papers were submitted for the task, all based on various types of fine-tuned transformer systems, often with ensemble methods and various data augmentation techniques. The best systems reached an F1-macro score of 94.49 (sub-task1) and a Spearman correlation coefficient of 0.80 (sub-task2), with interesting variations in specific constructions and/or languages. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Neural networks en_US
dc.subject Languages en_US
dc.title SemEval-2022 Task en_US
dc.title.alternative PreTENS - Evaluating Neural Networks on Presuppositional Semantic Knowledge en_US
dc.type Article en_US


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