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    <abstract>Automatic Peer-review Aspect Score Prediction (PASP) of academic papers can be a helpful assistant tool for both reviewers and authors. Most existing works on PASP utilize supervised learning techniques. However, the limited number of peer-review data deteriorates the performance of PASP. This paper presents a novel semi-supervised learning (SSL) method that incorporates the Transformer fine-tuning into the Γ-model, a variant of the Ladder network, to leverage contextual features from unlabeled data. Backpropagation simultaneously minimizes the sum of supervised and unsupervised cost functions, avoiding the need for layer-wise pre-training. The experimental results show that our model outperforms the supervised and naive semi-supervised learning baselines. Our source codes are available online.</abstract>
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%0 Conference Proceedings
%T Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction
%A Muangkammuen, Panitan
%A Fukumoto, Fumiyo
%A Li, Jiyi
%A Suzuki, Yoshimi
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F muangkammuen-etal-2022-exploiting
%X Automatic Peer-review Aspect Score Prediction (PASP) of academic papers can be a helpful assistant tool for both reviewers and authors. Most existing works on PASP utilize supervised learning techniques. However, the limited number of peer-review data deteriorates the performance of PASP. This paper presents a novel semi-supervised learning (SSL) method that incorporates the Transformer fine-tuning into the Γ-model, a variant of the Ladder network, to leverage contextual features from unlabeled data. Backpropagation simultaneously minimizes the sum of supervised and unsupervised cost functions, avoiding the need for layer-wise pre-training. The experimental results show that our model outperforms the supervised and naive semi-supervised learning baselines. Our source codes are available online.
%R 10.18653/v1/2022.findings-emnlp.164
%U https://aclanthology.org/2022.findings-emnlp.164
%U https://doi.org/10.18653/v1/2022.findings-emnlp.164
%P 2233-2240

Markdown (Informal)

[Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction](https://aclanthology.org/2022.findings-emnlp.164) (Muangkammuen et al., Findings 2022)

• Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction (Muangkammuen et al., Findings 2022)

ACL

• Panitan Muangkammuen, Fumiyo Fukumoto, Jiyi Li, and Yoshimi Suzuki. 2022. Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2233–2240, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.