# Label-Wise Document Pre-Training for Multi-Label Text Classification

arXiv: https://arxiv.org/abs/2008.06695 · HTML: https://mlsanity.com/paper/2008.06695/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach](https://mlsanity.com/paper/2312.11276/) (2023)
- [Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue](https://mlsanity.com/paper/2102.02435/) (2021)
- [Label prompt for multi-label text classification](https://mlsanity.com/paper/2106.10076/) (2021)

## Abstract (arXiv)

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations. In this paper, we tackle this challenge by developing Label-Wise Pre-Training (LW-PT) method to get a document representation with label-aware information. The basic idea is that, a multi-label document can be represented as a combination of multiple label-wise representations, and that, correlated labels always cooccur in the same or similar documents. LW-PT implements this idea by constructing label-wise document classification tasks and trains label-wise document encoders. Finally, the pre-trained label-wise encoder is fine-tuned with the downstream MLTC task. Extensive experimental results validate that the proposed method has significant advantages over the previous state-of-the-art models and is able to discover reasonable label relationship. The code is released to facilitate other researchers.