Abstract
This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding. We propose DomBERT, an extension of BERT to learn from both in-domain corpus and relevant domain corpora. This helps in learning domain language models with low-resources. Experiments are conducted on an assortment of tasks in aspect-based sentiment analysis (ABSA), demonstrating promising results.- Anthology ID:
- 2020.findings-emnlp.156
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1725–1731
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.156
- DOI:
- 10.18653/v1/2020.findings-emnlp.156
- Cite (ACL):
- Hu Xu, Bing Liu, Lei Shu, and Philip Yu. 2020. DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1725–1731, Online. Association for Computational Linguistics.
- Cite (Informal):
- DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (Xu et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.findings-emnlp.156.pdf