Neural network methods which leverage word-embedding obtained from unsupervised learning models have been widely adopted in many natural language processing (NLP) tasks, including sentiment analysis and sentence classification. Existing sentence representation generation approaches which serve for classification tasks generally rely on complex deep neural networks but relatively simple loss functions, such as cross entropy loss function. These approaches cannot produce satisfactory separable sentence representations because the usage of cross entropy may ignore the sentiment and semantic information of the labels. To extract useful information from labels for improving the distinguishability of the obtained sentence representations, this paper proposes a label-oriented loss function. The proposed loss function takes advantage of the word-embeddings of labels to guide the production of meaningful sentence representations which serve for downstream classification tasks. Compared with existing end-to-end approaches, the evaluation experiments on several datasets illustrate that using the proposed loss function can achieve competitive and even better classification results.

利用从无监督学习模型中获得的词嵌入功能的神经网络方法已被广泛用于许多自然语言处理（NLP）任务中，包括情感分析和句子分类。用于分类任务的现有句子表示生成方法通常依赖于复杂的深度神经网络，但是依赖于相对简单的损失函数，例如交叉熵损失函数。这些方法无法产生令人满意的可分离句子表示形式，因为使用交叉熵可能会忽略标签的情感和语义信息。为了从标签中提取有用的信息以提高获得的句子表示的可区分性，本文提出了一种面向标签的损失函数。所提出的损失函数利用标签的词嵌入来指导有意义的句子表示的产生，该句子表示用于下游分类任务。与现有的端到端方法相比，对多个数据集的评估实验表明，使用所提出的损失函数可以实现竞争甚至更好的分类结果。