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

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Roberto Zamparelli, Shammur Absar Chowdhury, Dominique Brunato, Felice Dell'Orletta, Md. Arid Hasan, Giulia Venturi, Cristiano Chesi

Published: 01 Jul 2022, Last Modified: 14 Dec 2023Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), Seattle, United States: Association for Computational Linguistics, 2022, p. 228-238.EveryoneCC BY-SA 4.0
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.