Abstract
Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.- Anthology ID:
- 2021.emnlp-main.361
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4406–4416
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.361
- DOI:
- 10.18653/v1/2021.emnlp-main.361
- Cite (ACL):
- Jian Liu, Zhiyang Teng, Leyang Cui, Hanmeng Liu, and Yue Zhang. 2021. Solving Aspect Category Sentiment Analysis as a Text Generation Task. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4406–4416, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Solving Aspect Category Sentiment Analysis as a Text Generation Task (Liu et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.emnlp-main.361.pdf
- Code
- lgw863/acsa-generation
- Data
- MAMS