@inproceedings{lad-etal-2022-dartmouth,
title = "{D}artmouth at {S}em{E}val-2022 Task 6: Detection of Sarcasm",
author = "Lad, Rishik and
Ma, Weicheng and
Vosoughi, Soroush",
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-1.128",
doi = "10.18653/v1/2022.semeval-1.128",
pages = "912--918",
abstract = "This paper introduces the result of Team Dartmouth{'}s experiments on each of the five subtasks for the detection of sarcasm in English and Arabic tweets. This detection was framed as a classification problem, and our contributions are threefold: we developed an English binary classifier system with RoBERTa, an Arabic binary classifier with XLM-RoBERTa, and an English multilabel classifier with BERT. Preprocessing steps are taken with labeled input data prior to tokenization, such as extracting and appending verbs/adjectives or representative/significant keywords to the end of an input tweet to help the models better understand and generalize sarcasm detection. We also discuss the results of simple data augmentation techniques to improve the quality of the given training dataset as well as an alternative approach to the question of multilabel sequence classification. Ultimately, our systems place us in the top 14 participants for each of the five subtasks.",
}
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<abstract>This paper introduces the result of Team Dartmouth’s experiments on each of the five subtasks for the detection of sarcasm in English and Arabic tweets. This detection was framed as a classification problem, and our contributions are threefold: we developed an English binary classifier system with RoBERTa, an Arabic binary classifier with XLM-RoBERTa, and an English multilabel classifier with BERT. Preprocessing steps are taken with labeled input data prior to tokenization, such as extracting and appending verbs/adjectives or representative/significant keywords to the end of an input tweet to help the models better understand and generalize sarcasm detection. We also discuss the results of simple data augmentation techniques to improve the quality of the given training dataset as well as an alternative approach to the question of multilabel sequence classification. Ultimately, our systems place us in the top 14 participants for each of the five subtasks.</abstract>
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%0 Conference Proceedings
%T Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm
%A Lad, Rishik
%A Ma, Weicheng
%A Vosoughi, Soroush
%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 lad-etal-2022-dartmouth
%X This paper introduces the result of Team Dartmouth’s experiments on each of the five subtasks for the detection of sarcasm in English and Arabic tweets. This detection was framed as a classification problem, and our contributions are threefold: we developed an English binary classifier system with RoBERTa, an Arabic binary classifier with XLM-RoBERTa, and an English multilabel classifier with BERT. Preprocessing steps are taken with labeled input data prior to tokenization, such as extracting and appending verbs/adjectives or representative/significant keywords to the end of an input tweet to help the models better understand and generalize sarcasm detection. We also discuss the results of simple data augmentation techniques to improve the quality of the given training dataset as well as an alternative approach to the question of multilabel sequence classification. Ultimately, our systems place us in the top 14 participants for each of the five subtasks.
%R 10.18653/v1/2022.semeval-1.128
%U https://aclanthology.org/2022.semeval-1.128
%U https://doi.org/10.18653/v1/2022.semeval-1.128
%P 912-918
Markdown (Informal)
[Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm](https://aclanthology.org/2022.semeval-1.128) (Lad et al., SemEval 2022)
ACL
- Rishik Lad, Weicheng Ma, and Soroush Vosoughi. 2022. Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 912–918, Seattle, United States. Association for Computational Linguistics.