论文标题
基于跨域特征增强对比度学习的零射姿势检测
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
论文作者
论文摘要
零射的立场检测具有挑战性，因为它需要在推理阶段检测以前看不见的靶标的立场。学习可转移目标不变特征的能力对于零射击立场检测至关重要。在这项工作中，我们提出了一种立场检测方法，该方法可以有效地适应看不见的目标，其核心是将目标不变的句法表达模式捕获为可转移的知识。具体而言，我们首先通过掩盖句子的主题单词来增强数据，然后将增强数据馈送到无监督的对比度学习模块中以捕获可转移的功能。然后，为了拟合特定的目标，我们将原始文本编码为目标特定特征。最后，我们采用了一种注意机制，该机制将句法表达模式与目标特异性特征相结合，以获得增强的特征，以预测以前看不见的目标。实验表明，我们的模型在四个基准数据集上优于竞争基线。
Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-shot stance detection. In this work, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge. Specifically, we first augment the data by masking the topic words of sentences, and then feed the augmented data to an unsupervised contrastive learning module to capture transferable features. Then, to fit a specific target, we encode the raw texts as target-specific features. Finally, we adopt an attention mechanism, which combines syntactic expression patterns with target-specific features to obtain enhanced features for predicting previously unseen targets. Experiments demonstrate that our model outperforms competitive baselines on four benchmark datasets.
