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. |
Similar Papers
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels. |
| Approach: | They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels. |
| Outcome: | The proposed approach outperforms state-of-the-art models on two benchmark datasets. |
Hierarchical Label Generation for Text Classification (2023.findings-eacl)
Copied to clipboard
| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
| Approach: | They propose a method that captures the label hierarchy for real-world classification applications by using a taxonomic hierarchy. |
| Outcome: | The proposed method can generate unseen labels in subword level. |
Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach (2020.coling-main)
Copied to clipboard
| Challenge: | Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. |
| Approach: | They propose a language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. |
| Outcome: | The proposed model outperforms the state-of-the-art in all languages of the CoNLL-2009 benchmark dataset. |
Enhancing Multi-Label Text Classification under Label-Dependent Noise: A Label-Specific Denoising Framework (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing noisy multi-label text classification methods rely on the class-conditional noise assumption, but in practice, noisy labels exhibit a certain degree of correlation with the true labels. |
| Approach: | They propose a label-specific denoising framework to counteract label-dependent noise by evaluating loss information, ranking information, and feature centroid. |
| Outcome: | The proposed framework significantly improves over existing state-of-the-art models under both synthetic and real-world noise conditions. |
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. |
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification (D18-1)
Copied to clipboard
| Challenge: | a novel model for multi-label text classification is proposed for the task of assigning multiple labels for a given text. |
| Approach: | They propose a novel model for multi-label text classification based on sequence-to-sequence learning and a hybrid attention mechanism that extracts both the word-level and the semantic unit. |
| Outcome: | The proposed model is competitive to the baseline models and more robust to classifying low-frequency labels. |
Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation (2023.emnlp-industry)
Copied to clipboard
Dan Li, Zi Long Zhu, Janneke van de Loo, Agnes Masip Gomez, Vikrant Yadav, Georgios Tsatsaronis, Zubair Afzal
| Challenge: | Existing approaches to extreme multi-label text classification face inherent challenges in terms of model, data, and evaluation. |
| Approach: | They propose a label ranking model as an alternative to the conventional SciBERT-based classification model and an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system. |
| Outcome: | The proposed model enables efficient handling of large-scale labels and accommodates new labels. |
Leveraging Text-to-Text Transformers as Classifier Chain for Few-Shot Multi-Label Classification (2025.emnlp-main)
Copied to clipboard
| Challenge: | Multilabel text classification (MLTC) is an essential task in NLP applications. |
| Approach: | They propose a distillation-based T5 generalist model for zero-shot MLTC and few-shot fine-tuning. |
| Outcome: | The proposed model outperforms baselines of similar size on three few-shot tasks. |
Augmented Natural Language for Generative Sequence Labeling (2020.emnlp-main)
Copied to clipboard
| Challenge: | generative framework for joint sequence labeling and sentence-level classification is general purpose, performing well on few-shot learning, low resource, and high resource tasks. |
| Approach: | They propose a generative framework for joint sequence labeling and sentence-level classification . their framework incorporates label semantics and shares knowledge across tasks . |
| Outcome: | The proposed model performs on few-shot learning, slot labeling, and intent classification benchmarks. |
SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)
Copied to clipboard
| 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. |