# Hierarchical Multi-Label Generation with Probabilistic Level-Constraint

arXiv: https://arxiv.org/abs/2505.03775 · HTML: https://mlsanity.com/paper/2505.03775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://mlsanity.com/paper/2302.10198/) (2023)
- [Pre-training Tasks for Embedding-based Large-scale Retrieval](https://mlsanity.com/paper/2002.03932/) (2020)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2110.00685/) (2021)
- [Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach](https://mlsanity.com/paper/1805.05181/) (2018)
- [TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering](https://mlsanity.com/paper/1812.09551/) (2018)
- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [Taxonomy Completion via Triplet Matching Network](https://mlsanity.com/paper/2101.01896/) (2021)
- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [Global Encoding for Abstractive Summarization](https://mlsanity.com/paper/1805.03989/) (2018)
- [Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks](https://mlsanity.com/paper/1710.10393/) (2017)
- [Cluster-Guided Label Generation in Extreme Multi-Label Classification](https://mlsanity.com/paper/2302.09150/) (2023)

## Abstract (arXiv)

Hierarchical Extreme Multi-Label Classification poses greater difficulties compared to traditional multi-label classification because of the intricate hierarchical connections of labels within a domain-specific taxonomy and the substantial number of labels. Some of the prior research endeavors centered on classifying text through several ancillary stages such as the cluster algorithm and multiphase classification. Others made attempts to leverage the assistance of generative methods yet were unable to properly control the output of the generative model. We redefine the task from hierarchical multi-Label classification to Hierarchical Multi-Label Generation (HMG) and employ a generative framework with Probabilistic Level Constraints (PLC) to generate hierarchical labels within a specific taxonomy that have complex hierarchical relationships. The approach we proposed in this paper enables the framework to generate all relevant labels across levels for each document without relying on preliminary operations like clustering. Meanwhile, it can control the model output precisely in terms of count, length, and level aspects. Experiments demonstrate that our approach not only achieves a new SOTA performance in the HMG task, but also has a much better performance in constrained the output of model than previous research work.