Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm

Rishik Lad, Weicheng Ma, Soroush Vosoughi


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.
Anthology ID:
2022.semeval-1.128
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:
912–918
Language:
URL:
https://aclanthology.org/2022.semeval-1.128
DOI:
10.18653/v1/2022.semeval-1.128
Bibkey:
Cite (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.
Cite (Informal):
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm (Lad et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.128.pdf
Video:
 https://aclanthology.org/2022.semeval-1.128.mp4