PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training

Harsh Goyal, Aadarsh Singh, Priyanshu Kumar

Abstract
We experiment with XLM RoBERTa for Word in Context Disambiguation in the Multi Lingual and Cross Lingual setting so as to develop a single model having knowledge about both settings. We solve the problem as a binary classification problem and also experiment with data augmentation and adversarial training techniques. In addition, we also experiment with a 2-stage training technique. Our approaches prove to be beneficial for better performance and robustness.
Anthology ID:
2021.semeval-1.98
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Editors:
Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
743–747
Language:
URL:
https://aclanthology.org/2021.semeval-1.98
DOI:
10.18653/v1/2021.semeval-1.98
Bibkey:
Cite (ACL):
Harsh Goyal, Aadarsh Singh, and Priyanshu Kumar. 2021. PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 743–747, Online. Association for Computational Linguistics.
Cite (Informal):
PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training (Goyal et al., SemEval 2021)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2021.semeval-1.98.pdf
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