Abstract
Retrieval-based methods have been proven effective in many NLP tasks. Previous methods use representations from the pre-trained model for similarity search directly. However, the sentence representations from the pre-trained model like BERT perform poorly in retrieving semantically similar sentences, resulting in poor performance of the retrieval-based methods. In this paper, we propose kNN-EC, a simple and efficient non-parametric emotion classification (EC) method using nearest neighbor retrieval. We use BERT-whitening to get better sentence semantics, ensuring that nearest neighbor retrieval works. Meanwhile, BERT-whitening can also reduce memory storage of datastore and accelerate retrieval speed, solving the efficiency problem of the previous methods. kNN-EC average improves the pre-trained model by 1.17 F1-macro on two emotion classification datasets.- Anthology ID:
- 2022.emnlp-main.312
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4738–4745
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.312
- DOI:
- 10.18653/v1/2022.emnlp-main.312
- Cite (ACL):
- Wenbiao Yin and Lin Shang. 2022. Efficient Nearest Neighbor Emotion Classification with BERT-whitening. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4738–4745, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Efficient Nearest Neighbor Emotion Classification with BERT-whitening (Yin & Shang, EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.312.pdf