@inproceedings{soloveva-2020-semeval,
title = "{SO} at {S}em{E}val-2020 Task 7: {D}eep{P}avlov Logistic Regression with {BERT} Embeddings vs {SVR} at Funniness Evaluation",
author = "Soloveva, Anita",
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-1.138/",
doi = "10.18653/v1/2020.semeval-1.138",
pages = "1055--1059",
abstract = "This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 {\textquotedblleft}Regression{\textquotedblright} and Sub-task 2 {\textquotedblleft}Predict the funnier of the two edited versions of an original headline{\textquotedblright}. I experimented with a number of different models, but ended up using DeepPavlov logistic regression (LR) with BERT English cased embeddings for the first sub-task and support vector regression model (SVR) for the second. RMSE score obtained for the first task was 0.65099 and accuracy for the second {--} 0.32915."
}
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<abstract>This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 “Regression” and Sub-task 2 “Predict the funnier of the two edited versions of an original headline”. I experimented with a number of different models, but ended up using DeepPavlov logistic regression (LR) with BERT English cased embeddings for the first sub-task and support vector regression model (SVR) for the second. RMSE score obtained for the first task was 0.65099 and accuracy for the second – 0.32915.</abstract>
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%0 Conference Proceedings
%T SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation
%A Soloveva, Anita
%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 soloveva-2020-semeval
%X This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 “Regression” and Sub-task 2 “Predict the funnier of the two edited versions of an original headline”. I experimented with a number of different models, but ended up using DeepPavlov logistic regression (LR) with BERT English cased embeddings for the first sub-task and support vector regression model (SVR) for the second. RMSE score obtained for the first task was 0.65099 and accuracy for the second – 0.32915.
%R 10.18653/v1/2020.semeval-1.138
%U https://aclanthology.org/2020.semeval-1.138/
%U https://doi.org/10.18653/v1/2020.semeval-1.138
%P 1055-1059
Markdown (Informal)
[SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation](https://aclanthology.org/2020.semeval-1.138/) (Soloveva, SemEval 2020)
ACL