# SGM: Sequence Generation Model for Multi-label Classification

arXiv: https://arxiv.org/abs/1806.04822 · HTML: https://mlsanity.com/paper/1806.04822/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 30 of 89)

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- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
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- [HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization](https://mlsanity.com/paper/2104.05220/) (2021)
- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition](https://mlsanity.com/paper/2201.05834/) (2022)
- [SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks](https://mlsanity.com/paper/2203.16773/) (2022)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2112.11740/) (2021)
- [F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Tasks](https://mlsanity.com/paper/2305.11916/) (2023)
- [Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels](https://mlsanity.com/paper/2304.10539/) (2023)
- [HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification](https://mlsanity.com/paper/2305.15182/) (2023)
- [Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning](https://mlsanity.com/paper/2106.03103/) (2021)
- [Constrained Sequence-to-Tree Generation for Hierarchical Text Classification](https://mlsanity.com/paper/2204.00811/) (2022)
- [CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognition](https://mlsanity.com/paper/2312.10201/) (2023)
- [Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder](https://mlsanity.com/paper/2107.00189/) (2021)
- [Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?](https://mlsanity.com/paper/2412.05137/) (2024)
- [Jam or Cream First? Modeling Ambiguity in Neural Machine Translation with SCONES](https://mlsanity.com/paper/2205.00704/) (2022)
- [Hierarchical Multi-Label Classification of Scientific Documents](https://mlsanity.com/paper/2211.02810/) (2022)
- [A Unified Generative Approach to Product Attribute-Value Identification](https://mlsanity.com/paper/2306.05605/) (2023)
- [An Effective Deployment of Contrastive Learning in Multi-label Text Classification](https://mlsanity.com/paper/2212.00552/) (2022)
- [None Class Ranking Loss for Document-Level Relation Extraction](https://mlsanity.com/paper/2205.00476/) (2022)
- [Planning Irregular Object Packing via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2211.09382/) (2022)
- [Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition](https://mlsanity.com/paper/2203.03195/) (2022)
- [Conditional set generation using Seq2seq models](https://mlsanity.com/paper/2205.12485/) (2022)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Hierarchical Text Classification As Sub-Hierarchy Sequence Generation](https://mlsanity.com/paper/2111.11104/) (2021)
- [Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation](https://mlsanity.com/paper/2211.01467/) (2022)

## Abstract (arXiv)

Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignore the correlations between labels. Besides, different parts of the text can contribute differently for predicting different labels, which is not considered by existing models. In this paper, we propose to view the multi-label classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experimental results show that our proposed methods outperform previous work by a substantial margin. Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.