@inproceedings{rozental-etal-2020-amobee,
title = "{A}mobee at {S}em{E}val-2020 Task 7: Regularization of Language Model Based Classifiers",
author = "Rozental, Alon and
Biton, Dadi and
Blank, Ido",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.127",
doi = "10.18653/v1/2020.semeval-1.127",
pages = "981--985",
abstract = "This paper describes Amobee{'}s participation in SemEval-2020 task 7: {``}Assessing Humor in Edited News Headlines{''}, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headlines. in this paper we present methods to fine-tune and ensemble various language models (LM) based classifiers to for this task. This technique used for both sub-tasks and reached the second place (out of 49) in sub-tasks 1 with RMSE score of 0.5, and the second (out of 32) place in sub-task 2 with accuracy of 66{\%} without using any additional data except the official training set.",
}
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<abstract>This paper describes Amobee’s participation in SemEval-2020 task 7: “Assessing Humor in Edited News Headlines”, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headlines. in this paper we present methods to fine-tune and ensemble various language models (LM) based classifiers to for this task. This technique used for both sub-tasks and reached the second place (out of 49) in sub-tasks 1 with RMSE score of 0.5, and the second (out of 32) place in sub-task 2 with accuracy of 66% without using any additional data except the official training set.</abstract>
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%0 Conference Proceedings
%T Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers
%A Rozental, Alon
%A Biton, Dadi
%A Blank, Ido
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F rozental-etal-2020-amobee
%X This paper describes Amobee’s participation in SemEval-2020 task 7: “Assessing Humor in Edited News Headlines”, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headlines. in this paper we present methods to fine-tune and ensemble various language models (LM) based classifiers to for this task. This technique used for both sub-tasks and reached the second place (out of 49) in sub-tasks 1 with RMSE score of 0.5, and the second (out of 32) place in sub-task 2 with accuracy of 66% without using any additional data except the official training set.
%R 10.18653/v1/2020.semeval-1.127
%U https://aclanthology.org/2020.semeval-1.127
%U https://doi.org/10.18653/v1/2020.semeval-1.127
%P 981-985
Markdown (Informal)
[Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers](https://aclanthology.org/2020.semeval-1.127) (Rozental et al., SemEval 2020)
ACL