# Multi-relation Message Passing for Multi-label Text Classification

arXiv: https://arxiv.org/abs/2202.04844 · HTML: https://mlsanity.com/paper/2202.04844/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model](https://mlsanity.com/paper/2007.06126/) (2020)
- [Neural Message Passing for Multi-Label Classification](https://mlsanity.com/paper/1904.08049/) (2019)
- [HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders](https://mlsanity.com/paper/2103.06375/) (2021)
- [Rectifying Classifier Chains for Multi-Label Classification](https://mlsanity.com/paper/1906.02915/) (2019)

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

- [Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality](https://mlsanity.com/paper/2310.07588/) (2023)
- [Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification](https://mlsanity.com/paper/2309.13543/) (2023)
- [KeNet:Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification](https://mlsanity.com/paper/2403.01767/) (2024)

## Abstract (arXiv)

A well-known challenge associated with the multi-label classification problem is modelling dependencies between labels. Most attempts at modelling label dependencies focus on co-occurrences, ignoring the valuable information that can be extracted by detecting label subsets that rarely occur together. For example, consider customer product reviews; a product probably would not simultaneously be tagged by both "recommended" (i.e., reviewer is happy and recommends the product) and "urgent" (i.e., the review suggests immediate action to remedy an unsatisfactory experience). Aside from the consideration of positive and negative dependencies, the direction of a relationship should also be considered. For a multi-label image classification problem, the "ship" and "sea" labels have an obvious dependency, but the presence of the former implies the latter much more strongly than the other way around. These examples motivate the modelling of multiple types of bi-directional relationships between labels. In this paper, we propose a novel method, entitled Multi-relation Message Passing (MrMP), for the multi-label classification problem. Experiments on benchmark multi-label text classification datasets show that the MrMP module yields similar or superior performance compared to state-of-the-art methods. The approach imposes only minor additional computational and memory overheads.