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.

SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge / Zamparelli, Roberto; Chowdhury, Shammur; Brunato, Dominique; Chesi, Cristiano; Dell’Orletta, Felice; Hasan, Md. Arid; Venturi, Giulia. - ELETTRONICO. - (2022), pp. 228-238. ((Intervento presentato al convegno SemEval-2022 tenutosi a Seattle, United States nel 10-15 luglio 2022 [10.18653/v1/2022.semeval-1.29].

SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge

Zamparelli, Roberto;Chowdhury, Shammur;
2022

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.
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Seattle, United States
Association for Computational Linguistics
Zamparelli, Roberto; Chowdhury, Shammur; Brunato, Dominique; Chesi, Cristiano; Dell’Orletta, Felice; Hasan, Md. Arid; Venturi, Giulia
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge / Zamparelli, Roberto; Chowdhury, Shammur; Brunato, Dominique; Chesi, Cristiano; Dell’Orletta, Felice; Hasan, Md. Arid; Venturi, Giulia. - ELETTRONICO. - (2022), pp. 228-238. ((Intervento presentato al convegno SemEval-2022 tenutosi a Seattle, United States nel 10-15 luglio 2022 [10.18653/v1/2022.semeval-1.29].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/353301
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