@inproceedings{zamparelli-etal-2022-semeval,
title = "{S}em{E}val-2022 Task 3: {P}re{TENS}-Evaluating Neural Networks on Presuppositional Semantic Knowledge",
author = "Zamparelli, Roberto and
Chowdhury, Shammur and
Brunato, Dominique and
Chesi, Cristiano and
Dell{'}Orletta, Felice and
Hasan, Md. Arid and
Venturi, Giulia",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.29",
doi = "10.18653/v1/2022.semeval-1.29",
pages = "228--238",
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: \textit{(i)} binary prediction task and \textit{(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.",
}
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%0 Conference Proceedings
%T SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge
%A Zamparelli, Roberto
%A Chowdhury, Shammur
%A Brunato, Dominique
%A Chesi, Cristiano
%A Dell’Orletta, Felice
%A Hasan, Md. Arid
%A Venturi, Giulia
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F zamparelli-etal-2022-semeval
%X 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.
%R 10.18653/v1/2022.semeval-1.29
%U https://aclanthology.org/2022.semeval-1.29
%U https://doi.org/10.18653/v1/2022.semeval-1.29
%P 228-238
Markdown (Informal)
[SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge](https://aclanthology.org/2022.semeval-1.29) (Zamparelli et al., SemEval 2022)
ACL