DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis

Hu Xu, Bing Liu, Lei Shu, Philip Yu


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
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1725–1731
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.156
DOI:
10.18653/v1/2020.findings-emnlp.156
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.156.pdf