Enhanced Coherence-Aware Network with Hierarchical Disentanglement for
  Aspect-Category Sentiment Analysis
Jin Cui Fumiyo Fukumoto Xinfeng Wang Yoshimi Suzuki Jiyi Li Noriko Tomuro Wanzeng Kong
Aspect-category-based sentiment analysis (ACSA), which aims to identify
aspect categories and predict their sentiments has been intensively studied due
to its wide range of NLP applications. Most approaches mainly utilize
intrasentential features. However, a review often includes multiple different
aspect categories, and some of them do not explicitly appear in the review.
Even in a sentence, there is more than one aspect category with its sentiments,
and they are entangled intra-sentence, which makes the model fail to
discriminately preserve all sentiment characteristics. In this paper, we
propose an enhanced coherence-aware network with hierarchical disentanglement
(ECAN) for ACSA tasks. Specifically, we explore coherence modeling to capture
the contexts across the whole review and to help the implicit aspect and
sentiment identification. To address the issue of multiple aspect categories
and sentiment entanglement, we propose a hierarchical disentanglement module to
extract distinct categories and sentiment features. Extensive experimental and
visualization results show that our ECAN effectively decouples multiple
categories and sentiments entangled in the coherence representations and
achieves state-of-the-art (SOTA) performance. Our codes and data are available
online: \url{https://github.com/cuijin-23/ECAN}.