Aspect sentiment triplet extraction is an emerging task in aspect-based sentiment analysis, which aims at simultaneously identifying the aspect, the opinion expression, and the sentiment from a given review sentence. Existing studies divide this task into many sub-tasks and process them in a pipeline manner, which ignores the relevance between different sub-tasks and leads to error accumulation. In this paper, we propose a hierarchical sequence labeling model (HSLM) to recognize the sentiment triplets in an end-to-end manner. Concretely, HSLM consists of an aspect-level sequence labeling module, an opinion-level sequence labeling module, and a sentiment-level sequence labeling module. To learn the interactions between the above three modules, we further design three information fusion mechanisms, including aspect feature fusion mechanism, opinion feature fusion mechanism, and global feature fusion mechanism to refine high-level semantic information. To verify the effectiveness of our model, we conduct comprehensive experiments on four benchmark datasets. The experimental results demonstrate that our model achieves state-of-the-art performances.