@inproceedings{gollapalli-etal-2023-nus,
title = "{NUS}-{IDS} at {P}rag{T}ag-2023: Improving Pragmatic Tagging of Peer Reviews through Unlabeled Data",
author = "Gollapalli, Sujatha Das and
Huang, Yixin and
Ng, See-Kiong",
editor = "Alshomary, Milad and
Chen, Chung-Chi and
Muresan, Smaranda and
Park, Joonsuk and
Romberg, Julia",
booktitle = "Proceedings of the 10th Workshop on Argument Mining",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.argmining-1.25",
doi = "10.18653/v1/2023.argmining-1.25",
pages = "212--217",
abstract = "We describe our models for the Pragmatic Tagging of Peer Reviews Shared Task at the 10th Workshop on Argument Mining at EMNLP-2023. We trained multiple sentence classification models for the above competition task by employing various state-of-the-art transformer models that can be fine-tuned either in the traditional way or through instruction-based fine-tuning. Multiple model predictions on unlabeled data are combined to tentatively label unlabeled instances and augment the dataset to further improve performance on the prediction task. In particular, on the F1000RD corpus, we perform on-par with models trained on 100{\%} of the training data while using only 10{\%} of the data. Overall, on the competition datasets, we rank among the top-2 performers for the different data conditions.",
}
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<abstract>We describe our models for the Pragmatic Tagging of Peer Reviews Shared Task at the 10th Workshop on Argument Mining at EMNLP-2023. We trained multiple sentence classification models for the above competition task by employing various state-of-the-art transformer models that can be fine-tuned either in the traditional way or through instruction-based fine-tuning. Multiple model predictions on unlabeled data are combined to tentatively label unlabeled instances and augment the dataset to further improve performance on the prediction task. In particular, on the F1000RD corpus, we perform on-par with models trained on 100% of the training data while using only 10% of the data. Overall, on the competition datasets, we rank among the top-2 performers for the different data conditions.</abstract>
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%0 Conference Proceedings
%T NUS-IDS at PragTag-2023: Improving Pragmatic Tagging of Peer Reviews through Unlabeled Data
%A Gollapalli, Sujatha Das
%A Huang, Yixin
%A Ng, See-Kiong
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F gollapalli-etal-2023-nus
%X We describe our models for the Pragmatic Tagging of Peer Reviews Shared Task at the 10th Workshop on Argument Mining at EMNLP-2023. We trained multiple sentence classification models for the above competition task by employing various state-of-the-art transformer models that can be fine-tuned either in the traditional way or through instruction-based fine-tuning. Multiple model predictions on unlabeled data are combined to tentatively label unlabeled instances and augment the dataset to further improve performance on the prediction task. In particular, on the F1000RD corpus, we perform on-par with models trained on 100% of the training data while using only 10% of the data. Overall, on the competition datasets, we rank among the top-2 performers for the different data conditions.
%R 10.18653/v1/2023.argmining-1.25
%U https://aclanthology.org/2023.argmining-1.25
%U https://doi.org/10.18653/v1/2023.argmining-1.25
%P 212-217
Markdown (Informal)
[NUS-IDS at PragTag-2023: Improving Pragmatic Tagging of Peer Reviews through Unlabeled Data](https://aclanthology.org/2023.argmining-1.25) (Gollapalli et al., ArgMining-WS 2023)
ACL