ACEs: Unsupervised Multi-label Aspect Detection with Aspect-category Experts

Unsupervised aspect detection (UAD) aims to identify the aspect categories mentioned in product reviews automatically. Existing unsupervised methods mainly focus on single-label prediction which cannot work well for the realistic application scene since a review usually contain multiple aspect categories. Recent attempts alleviate this issue by setting a threshold. However, the imbalance of the number of category-related words in review segments makes these methods difficult to find optimal thresholds to recall all categories, which is a common but neglected phenomenon. In this paper, we propose a novel unsupervised method termed Aspect-Category Experts (ACEs) to address this problem. Our goal is to train a set of aspect-category experts to encode the sentence in parallel, where experts and aspect categories correspond one-to-one. Experts in different aspects weight the embedding of representative words with aspect-specific attention to avoid the negative impact of accumulation. Besides, to enhance the complementarity between different experts to reduce inter-class feature entanglement, we construct a novel mutual exclusion loss (ME loss) to improve the aspect detection performance. Extensive experimental results on four datasets demonstrate that our proposed ACEs model outperforms the previous state-of-the-art methods.