RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations
<|reference_start|>RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations: 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.<|reference_end|>
@article{zhang2024ronid:,
    title={RoNID: New Intent Discovery with Generated-Reliable Labels and
  Cluster-friendly Representations},
    author={Shun Zhang, Chaoran Yan, Jian Yang, Changyu Ren, Jiaqi Bai, Tongliang
  Li, Zhoujun Li},
    journal={arXiv preprint arXiv:2404.08977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08977},
    primaryClass={cs.CL cs.LG}
}