Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment
  Triplet Extraction
Yew Ken Chia Hui Chen Wei Han Guizhen Chen Sharifah Mahani Aljunied Soujanya Poria Lidong Bing
Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based
Sentiment Analysis (ABSA) that considers each opinion term, their expressed
sentiment, and the corresponding aspect targets. However, existing methods are
limited to the in-domain setting with two domains. Hence, we propose a
domain-expanded benchmark to address the in-domain, out-of-domain and
cross-domain settings. We support the new benchmark by annotating more than
4000 data samples for two new domains based on hotel and cosmetics reviews. Our
analysis of five existing methods shows that while there is a significant gap
between in-domain and out-of-domain performance, generative methods have a
strong potential for domain generalization. Our datasets, code implementation
and models are available at https://github.com/DAMO-NLP-SG/domain-expanded-aste .