Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction
Panitan Muangkammuen, Fumiyo Fukumoto, Jiyi Li, Yoshimi Suzuki
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.- Anthology ID:
- 2022.findings-emnlp.164
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2233–2240
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.164
- DOI:
- 10.18653/v1/2022.findings-emnlp.164
- Cite (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.
- Cite (Informal):
- Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction (Muangkammuen et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.findings-emnlp.164.pdf