Abstract
This paper presents the 10th and 11th place system for Subtask A -English and Subtask A Arabic respectively of the SemEval 2022 -Task 6. The purpose of the Subtask A was to classify a given text sequence into sarcastic and nonsarcastic. We also breifly cover our method for Subtask B which performed subpar when compared with most of the submissions on the official leaderboard . All of the developed solutions used a transformers based language model for encoding the text sequences with necessary changes of the pretrained weights and classifier according to the language and subtask at hand .- Anthology ID:
- 2022.semeval-1.127
- 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:
- 907–911
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.127
- DOI:
- 10.18653/v1/2022.semeval-1.127
- Cite (ACL):
- Nikhil Singh. 2022. niksss at SemEval-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ?. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 907–911, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- niksss at SemEval-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ? (Singh, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.semeval-1.127.pdf
- Data
- TweetEval