Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification.

中文翻译：

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用于姿势分类的常识知识增强记忆网络

立场分类旨在识别文本中对给定目标的态度是有利的、消极的或无关的。在现有的立场分类模型中，仅利用文本表示，而忽略常识知识。为了更好地将常识知识纳入立场分类，我们提出了一种名为常识知识增强记忆网络的新模型，该模型联合表示给定目标和文本的文本和常识知识表示。我们模型中的文本记忆模块将文本表示视为记忆向量，并使用注意力机制来体现重要部分。对于常识知识记忆模块，我们共同利用 TransE 模型学习的实体和关系嵌入来充分利用知识图谱的约束。