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.

通过 EM-style 方法，我们提出了一个 Robust New Intent Discovery（RoNID）框架，该框架致力于解决当前方法中存在的关于伪标签不准确和表示学习差的问题，并通过可靠的伪标签生成模块和友好的聚类表示学习模块构建具有可靠伪标签和友好聚类表示的稳健模型。