论文标题
动态语义匹配和聚集网络，用于几个射击意图检测
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
论文作者
论文摘要
由于可用的带注释的话语缺乏，很少有意图检测具有挑战性。尽管最近的著作表明，多级匹配在将学到的知识从可见的培训课程转移到新型测试类中起着重要作用，但它们依靠静态相似性度量和过度细粒度的匹配组件。这些局限性抑制了概括的能力，这些能力对概括性学习环境进行了概括，在这些设备中，看到的和新颖的阶级都是共存的。在本文中，我们提出了一个新颖的语义匹配和聚合网络，其中语义成分通过多头自我注意从话语中提取，并具有其他动态正则化约束。这些语义组件捕获了高级信息，从而在实例之间产生了更有效的匹配。我们的多观点匹配方法提供了一个全面的匹配度量，以增强标记和未标记实例的表示形式。我们还提出了一个更具挑战性的评估设置，该设置考虑了联合全级标签空间上的分类。广泛的实验结果证明了我们方法的有效性。我们的代码和数据公开可用。
Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components. These limitations inhibit generalizing capability towards Generalized Few-shot Learning settings where both seen and novel classes are co-existent. In this paper, we propose a novel Semantic Matching and Aggregation Network where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. These semantic components capture high-level information, resulting in more effective matching between instances. Our multi-perspective matching method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances. We also propose a more challenging evaluation setting that considers classification on the joint all-class label space. Extensive experimental results demonstrate the effectiveness of our method. Our code and data are publicly available.
