@inproceedings{feng-etal-2024-well,
    title = "Well Begun Is Half Done: An Implicitly Augmented Generative Framework with Distribution Modification for Hierarchical Text Classification",
    author = "Feng, Huawen  and
      Yan, Jingsong  and
      Liu, Junlong  and
      Zheng, Junhao  and
      Ma, Qianli",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1515",
    pages = "17433--17443",
    abstract = "Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. Discriminative methods usually incorporate the hierarchical structure information into the encoding process, while generative methods decode the features according to it. However, the data distribution varies widely among different categories of samples, but current methods ignore the data imbalance, making the predictions biased and susceptible to error propagation. In this paper, we propose an **IM**plicitly **A**ugmented **G**enerativ **E** framework with distribution modification for hierarchical text classification (**IMAGE**). Specifically, we translate the distributions of original samples along various directions through implicit augmentation to get more diverse data. Furthermore, given the scarcity of the samples of tail classes, we adjust their distributions by transferring knowledge from other classes in label space. In this way, the generative framework learns a better beginning of the feature sequence without a prediction bias and avoids being misled by its wrong predictions for head classes. Experimental results show that **IMAGE** obtains competitive results compared with state-of-the-art methods and prove its superiority on unbalanced data.",
}

Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. Discriminative methods usually incorporate the hierarchical structure information into the encoding process, while generative methods decode the features according to it. However, the data distribution varies widely among different categories of samples, but current methods ignore the data imbalance, making the predictions biased and susceptible to error propagation. In this paper, we propose an **IM**plicitly **A**ugmented **G**enerativ **E** framework with distribution modification for hierarchical text classification (**IMAGE**). Specifically, we translate the distributions of original samples along various directions through implicit augmentation to get more diverse data. Furthermore, given the scarcity of the samples of tail classes, we adjust their distributions by transferring knowledge from other classes in label space. In this way, the generative framework learns a better beginning of the feature sequence without a prediction bias and avoids being misled by its wrong predictions for head classes. Experimental results show that **IMAGE** obtains competitive results compared with state-of-the-art methods and prove its superiority on unbalanced data.
Feng, Huawen Yan, Jingsong Liu, Junlong Zheng, Junhao Ma, Qianli
Well Begun Is Half Done: An Implicitly Augmented Generative Framework with Distribution Modification for Hierarchical Text Classification