Bidirectional Generative Framework for Cross-domain Aspect-based
  Sentiment Analysis
Yue Deng Wenxuan Zhang Sinno Jialin Pan Lidong Bing
Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various
fine-grained sentiment analysis tasks on a target domain by transferring
knowledge from a source domain. Since labeled data only exists in the source
domain, a model is expected to bridge the domain gap for tackling cross-domain
ABSA. Though domain adaptation methods have proven to be effective, most of
them are based on a discriminative model, which needs to be specifically
designed for different ABSA tasks. To offer a more general solution, we propose
a unified bidirectional generative framework to tackle various cross-domain
ABSA tasks. Specifically, our framework trains a generative model in both
text-to-label and label-to-text directions. The former transforms each task
into a unified format to learn domain-agnostic features, and the latter
generates natural sentences from noisy labels for data augmentation, with which
a more accurate model can be trained. To investigate the effectiveness and
generality of our framework, we conduct extensive experiments on four
cross-domain ABSA tasks and present new state-of-the-art results on all tasks.
Our data and code are publicly available at
\url{https://github.com/DAMO-NLP-SG/BGCA}.