# Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification

arXiv: https://arxiv.org/abs/2304.07022 · HTML: https://mlsanity.com/paper/2304.07022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://mlsanity.com/paper/1506.07285/) (2015)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [DocBERT: BERT for Document Classification](https://mlsanity.com/paper/1904.08398/) (2019)
- [Joint Entity and Relation Extraction with Set Prediction Networks](https://mlsanity.com/paper/2011.01675/) (2020)
- [Deep Extreme Multi-label Learning](https://mlsanity.com/paper/1704.03718/) (2017)
- [GILE: A Generalized Input-Label Embedding for Text Classification](https://mlsanity.com/paper/1806.06219/) (2018)
- [Explicit Interaction Model towards Text Classification](https://mlsanity.com/paper/1811.09386/) (2018)
- [How is a data-driven approach better than random choice in label space division for multi-label classification?](https://mlsanity.com/paper/1606.02346/) (2016)
- [Object Detection as Probabilistic Set Prediction](https://mlsanity.com/paper/2203.07980/) (2022)

## Built on by (most cited first, 1 of 1)

- [Improving Generated and Retrieved Knowledge Combination Through Zero-shot Generation](https://mlsanity.com/paper/2412.18800/) (2024)

## Abstract (arXiv)

Multi-label text classification involves extracting all relevant labels from a sentence. Given the unordered nature of these labels, we propose approaching the problem as a set prediction task. To address the correlation between labels, we leverage Graph Convolutional Networks and construct an adjacency matrix based on the statistical relations between labels. Additionally, we enhance recall ability by applying the Bhattacharyya distance to the output distributions of the set prediction networks. We evaluate the effectiveness of our approach on two multi-label datasets and demonstrate its superiority over previous baselines through experimental results.