Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.

翻译：文档表示是许多机器理解自然语言处理任务的核心。以无监督方式学习得到的一般性表示保留了通用性，可用于多种应用场景。实践中，情感分析作为一项与语义深度相关的挑战性任务，常被用于评估通用表示的质量。现有的无监督文档表示学习方法可分为两类：序列方法（显式考虑词语顺序）与非序列方法（不显式考虑词语顺序）。然而，这两类方法均存在各自的局限性。本文提出了一种模型，能够克服这两类方法所遇到的困难。实验表明，该模型在主流情感分析数据集及细粒度方面级情感分析任务上，显著优于当前最先进的方法。