@inproceedings{panda-etal-2021-shuffled,
title = "Shuffled-token Detection for Refining Pre-trained {R}o{BERT}a",
author = "Panda, Subhadarshi and
Agrawal, Anjali and
Ha, Jeewon and
Bloch, Benjamin",
editor = "Durmus, Esin and
Gupta, Vivek and
Liu, Nelson and
Peng, Nanyun and
Su, Yu",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-srw.12",
doi = "10.18653/v1/2021.naacl-srw.12",
pages = "88--93",
abstract = "State-of-the-art transformer models have achieved robust performance on a variety of NLP tasks. Many of these approaches have employed domain agnostic pre-training tasks to train models that yield highly generalized sentence representations that can be fine-tuned for specific downstream tasks. We propose refining a pre-trained NLP model using the objective of detecting shuffled tokens. We use a sequential approach by starting with the pre-trained RoBERTa model and training it using our approach. Applying random shuffling strategy on the word-level, we found that our approach enables the RoBERTa model achieve better performance on 4 out of 7 GLUE tasks. Our results indicate that learning to detect shuffled tokens is a promising approach to learn more coherent sentence representations.",
}
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<abstract>State-of-the-art transformer models have achieved robust performance on a variety of NLP tasks. Many of these approaches have employed domain agnostic pre-training tasks to train models that yield highly generalized sentence representations that can be fine-tuned for specific downstream tasks. We propose refining a pre-trained NLP model using the objective of detecting shuffled tokens. We use a sequential approach by starting with the pre-trained RoBERTa model and training it using our approach. Applying random shuffling strategy on the word-level, we found that our approach enables the RoBERTa model achieve better performance on 4 out of 7 GLUE tasks. Our results indicate that learning to detect shuffled tokens is a promising approach to learn more coherent sentence representations.</abstract>
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%0 Conference Proceedings
%T Shuffled-token Detection for Refining Pre-trained RoBERTa
%A Panda, Subhadarshi
%A Agrawal, Anjali
%A Ha, Jeewon
%A Bloch, Benjamin
%Y Durmus, Esin
%Y Gupta, Vivek
%Y Liu, Nelson
%Y Peng, Nanyun
%Y Su, Yu
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F panda-etal-2021-shuffled
%X State-of-the-art transformer models have achieved robust performance on a variety of NLP tasks. Many of these approaches have employed domain agnostic pre-training tasks to train models that yield highly generalized sentence representations that can be fine-tuned for specific downstream tasks. We propose refining a pre-trained NLP model using the objective of detecting shuffled tokens. We use a sequential approach by starting with the pre-trained RoBERTa model and training it using our approach. Applying random shuffling strategy on the word-level, we found that our approach enables the RoBERTa model achieve better performance on 4 out of 7 GLUE tasks. Our results indicate that learning to detect shuffled tokens is a promising approach to learn more coherent sentence representations.
%R 10.18653/v1/2021.naacl-srw.12
%U https://aclanthology.org/2021.naacl-srw.12
%U https://doi.org/10.18653/v1/2021.naacl-srw.12
%P 88-93
Markdown (Informal)
[Shuffled-token Detection for Refining Pre-trained RoBERTa](https://aclanthology.org/2021.naacl-srw.12) (Panda et al., NAACL 2021)
ACL
- Subhadarshi Panda, Anjali Agrawal, Jeewon Ha, and Benjamin Bloch. 2021. Shuffled-token Detection for Refining Pre-trained RoBERTa. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 88–93, Online. Association for Computational Linguistics.