Challenge: Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance.
Approach: They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories.
Outcome: The proposed method significantly outperforms state-of-the-art models.

Similar Papers

From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)

Copied to clipboard

Challenge: Existing models rely on pre-trained language models, which have a maximum input sequence length of 512 tokens, and therefore have 'input length limitation'.
Approach: They propose a text segmentation algorithm which guarantees to produce the optimal segmentation to address the issue of input length limitation caused by PLMs.
Outcome: The proposed method improves both text and label representations on MLTC datasets, unraveling the intricate correlations between texts and labels.
Towards Better Representations for Multi-Label Text Classification with Multi-granularity Information (2023.findings-emnlp)

Copied to clipboard

Challenge: Existing studies have shown that pre-trained language models generate word frequency-oriented text representations, causing texts with different labels to be closely distributed in a narrow region, which is difficult to classify.
Approach: They propose a framework to refine the text representation for multi-label text classification using contrastive learning and multi-task learning modules.
Outcome: The proposed framework improves the quality of the representations and yields stable and competitive improvements.
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification (2022.acl-short)

Copied to clipboard

Challenge: Existing methods for multi-label text classification neglect the knowledge from the existing similar instances when predicting labels of a specific text.
Approach: They propose a k nearest neighbor mechanism which retrieves several neighbor instances and interpolates the model output with their labels.
Outcome: Extensive experiments show that the proposed method can bring significant performance improvements to multiple MLTC models including state-of-the-art pretrained and non-pretrained ones.
Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)

Copied to clipboard

Challenge: a large number of medical encounters need to be coded everyday due to long document sets and large label set.
Approach: They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents .
Outcome: The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain.
Label-Specific Document Representation for Multi-Label Text Classification (D19-1)

Copied to clipboard

Challenge: Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels.
Approach: They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation.
Outcome: The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis .
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning (2021.findings-acl)

Copied to clipboard

Challenge: Existing approaches to multi-task learning fail to capture label correlations . Existing methods suffer from label order dependency, label combination over-fitting and error propagation problems.
Approach: They propose a novel approach with multi-task learning to enhance label correlation feedback.
Outcome: The proposed method outperforms baselines on AAPD and RCV1-V2 datasets.
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale? (2025.coling-industry)

Copied to clipboard

Challenge: Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits.
Approach: They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges.
Outcome: The proposed methods avoid frequent retraining by leveraging zero-shot and few-shot learning for real-time label assignment.
Cluster-Guided Label Generation in Extreme Multi-Label Classification (2023.eacl-main)

Copied to clipboard

Challenge: Existing classification-based models are poorly per-form for tail labels and ignore semantic relations among labels.
Approach: They propose to guide label generation using label cluster information to hierarchically generate lower-level labels.
Outcome: The proposed model outperforms classification and generation baselines on tail labels and improves in four popular XMC benchmarks.
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (2021.emnlp-main)

Copied to clipboard

Challenge: Large-scale multi-label text classification tasks often face long-tailed label distributions, where many labels have few or even no training instances.
Approach: They propose a meta-learning approach that incorporates the objective of adapting to new low-resource tasks into the meta-Learning phase.
Outcome: The proposed approach achieves state-of-the-art against strong baselines and can still enhance powerful BERTlike models.
Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification (2025.findings-acl)

Copied to clipboard

Challenge: Existing approaches to multi-label text classification are limited by textual data.
Approach: They propose a domain-agnostic generative model framework for multi-label text classification that generates predefined label descriptions and matches them to predefined labels.
Outcome: The proposed model achieves 13.94% and 24.85% performance over all datasets.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations