DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
This work addresses the challenge of domain-specific language understanding with low resources, though it appears incremental as it builds directly on BERT.
The paper tackled the problem of learning domain-oriented language models for aspect-based sentiment analysis by proposing DomBERT, which extends BERT to incorporate in-domain and relevant domain corpora, achieving promising results in experiments.
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, demonstrating promising results.