Abstract
New Intent Discovery (NID) strives to identify known and reasonably deduce novel intent groups in the open-world scenario. But current methods face issues with inaccurate pseudo-labels and poor representation learning, creating a negative feedback loop that degrades overall model performance, including accuracy and the adjusted rand index. To address the aforementioned challenges, we propose a Robust New Intent Discovery (RoNID) framework optimized by an EM-style method, which focuses on constructing reliable pseudo-labels and obtaining cluster-friendly discriminative representations. RoNID comprises two main modules: reliable pseudo-label generation module and cluster-friendly representation learning module. Specifically, the pseudo-label generation module assigns reliable synthetic labels by solving an optimal transport problem in the E-step, which effectively provides high-quality supervised signals for the input of the cluster-friendly representation learning module. To learn cluster-friendly representation with strong intra-cluster compactness and large inter-cluster separation, the representation learning module combines intra-cluster and inter-cluster contrastive learning in the M-step to feed more discriminative features into the generation module. RoNID can be performed iteratively to ultimately yield a robust model with reliable pseudo-labels and cluster-friendly representations. Experimental results on multiple benchmarks demonstrate our method brings substantial improvements over previous state-of-the-art methods by a large margin of +1~+4 points.
Abstract (translated)
新意图发现（NID）旨在在开放世界场景中识别已知的和合理的推断出新的意图组。然而，目前的 methods 面临准确伪标签和差的学习表示的挑战，导致负反馈循环，降低整体模型性能，包括准确性和调整的均方差。为解决上述挑战，我们提出了一个通过EM风格方法优化的鲁棒新意图发现（RoNID）框架。该框架关注于构建可靠的伪标签和获得聚类友好的表示。RoNID 由两个主要模块组成：可靠伪标签生成模块和聚类友好表示学习模块。具体来说，伪标签生成模块通过求解优化传输问题在 E 步骤，从而有效地为聚类友好表示学习模块提供高质量的超监督信号。为了通过强烈的聚类内紧凑性和大的跨聚类分离学习聚类友好表示，表示学习模块在 M 步骤结合了聚类内和跨聚类对比学习，将更多有区别的特征提供给生成模块。RoNID 可以递归执行，最终得到一个具有可靠伪标签和聚类友好表示的稳健模型。在多个基准测试上进行的实验结果表明，我们的方法在很大程度上超过了以前的最先进方法。
URL
https://arxiv.org/abs/2404.08977
