RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations
arxiv(2024)
摘要
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.
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