| Challenge: | Experimental results show that MACLR achieves superior performance compared to other baseline methods. |
| Approach: | They propose to pre-train Transformer-based encoders with self-supervised contrastive losses to learn the semantic embeddings of instances and labels with raw text. |
| Outcome: | The proposed method improves on the EZ-XMC model with a limited number of ground-truth positive pairs. |
Similar Papers
Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales (2025.coling-main)
Copied to clipboard
| Challenge: | Extreme Zero-shot XMC uses lightweight bi-encoders to identify pseudo labels . state-of-the-art methods rely on suboptimal labels for training . |
| Approach: | They propose a framework that uses a lightweight bi-encoder to identify high-quality pseudo labels during training while employing a lightweight bi-coder for efficient inference. |
| Outcome: | The proposed framework achieves superior performance and efficiency over existing methods. |
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing research has focused on fully supervised XMC, but real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. |
| Approach: | They propose a framework that generates a set of candidate labels through in-context learning and then reranks them. |
| Outcome: | The proposed framework advances state-of-the-art on two diverse public benchmarks. |
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent work in XMC addresses this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. |
| Approach: | They propose a method that uses a shallow transformer encoder to combine text-based embeddings, label centroids and learnable free vectors to improve XMC efficiency. |
| Outcome: | The proposed method achieves state-of-the-art in several public benchmarks of different sizes and domains while keeping the model efficient. |
Open Vocabulary Extreme Classification Using Generative Models (2022.findings-acl)
Copied to clipboard
Daniel Simig, Fabio Petroni, Pouya Yanki, Kashyap Popat, Christina Du, Sebastian Riedel, Majid Yazdani
| Challenge: | Extreme multi-label classification (XMC) aims at tagging content with subset of labels from an extremely large label set. |
| Approach: | They propose a model that predicts a set of labels outside of the known vocabulary by using a loss-dependent loss-based loss-free model. |
| Outcome: | The proposed model can predict labels outside the known vocabulary while performing on par with state-of-the-art solutions for known labels. |
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on few-shot and zero-shot learning, but label occurrences vary widely . authors propose a new classification challenge that can be used to manage labels across the full frequency spectrum . |
| Approach: | They propose a new classification challenge that allows for label co-occurrences without predefined limits. |
| Outcome: | The proposed system can handle freq-shot, few-shot and zero-shot labels without limits. |
Open-world Multi-label Text Classification with Extremely Weak Supervision (2024.emnlp-main)
Copied to clipboard
| Challenge: | Similar single-label XWS settings cannot be easily adapted for multi-l label classification. |
| Approach: | They propose a novel method for open-world multi-label text classification under extremely weak supervision where the user provides a brief description without any labels or ground-truth label space. |
| Outcome: | The proposed method exhibits a remarkable increase in ground-truth label space coverage on various datasets. |
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. |
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification (2022.lrec-1)
Copied to clipboard
| Challenge: | Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate due to domain experts taking a lot of time working through all the classes. |
| Approach: | They propose to build classifiers on multi-label text datasets using Active Learning to reduce labeling effort. |
| Outcome: | The proposed classifiers can be used to reduce labeling effort on multi-label datasets. |
Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for zero-shot multi-label text classification struggle with accuracy and poor training efficiency. |
| Approach: | They propose a structural contrastive representation learning approach that uses randomized text segmentation to generate high-quality contrastive pairs. |
| Outcome: | The proposed approach improves accuracy and speed up training time on publicly available datasets. |
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. |