Abstract
In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.- Anthology ID:
- D19-5505
- Volume:
- Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–41
- Language:
- URL:
- https://aclanthology.org/D19-5505
- DOI:
- 10.18653/v1/D19-5505
- Cite (ACL):
- Xin Li, Lidong Bing, Wenxuan Zhang, and Wai Lam. 2019. Exploiting BERT for End-to-End Aspect-based Sentiment Analysis. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 34–41, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (Li et al., WNUT 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/D19-5505.pdf
- Code
- lixin4ever/BERT-E2E-ABSA