UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection

Emmanuel Osei-Brefo, Huizhi Liang


Abstract
Sarcasm has gained notoriety for being difficult to detect by machine learning systems due to its figurative nature. In this paper, Bidirectional Encoder Representations from Transformers (BERT) model has been used with ensemble loss made of cross-entropy loss and negative log-likelihood loss to classify whether a given sentence is in English and Arabic tweets are sarcastic or not. From the results obtained in the experiments, our proposed BERT with ensemble loss achieved superior performance when applied to English and Arabic test datasets. For the validation dataset, our model performed better on the Arabic dataset but failed to outperform the baseline method (made of BERT with only a single loss function) when applied on the English validation set.
Anthology ID:
2022.semeval-1.121
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
871–876
Language:
URL:
https://aclanthology.org/2022.semeval-1.121
DOI:
10.18653/v1/2022.semeval-1.121
Bibkey:
Cite (ACL):
Emmanuel Osei-Brefo and Huizhi Liang. 2022. UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 871–876, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection (Osei-Brefo & Liang, SemEval 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.semeval-1.121.pdf
Data
iSarcasmEval