| Challenge: | Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group) and labels that never appear in the training dataset (zero-shot groups). |
| Approach: | They perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. |
| Outcome: | The proposed methods improve on two publicly available datasets for multi-label text classification. |
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X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification (2024.findings-acl)
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| 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. |
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs (2020.emnlp-main)
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| Challenge: | Few/zero-shot learning is a big challenge of many classification tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. |
| Approach: | They propose a multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships to improve multi-label zero/few-shot document classification. |
| Outcome: | The proposed model improves on two large clinical datasets and the EU legislation dataset on few/zero-shot labels. |
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020.coling-main)
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| Challenge: | Existing approaches to few-shot text classification require domain expertise and an understanding of the language model's abilities to define the mapping between words and labels. |
| Approach: | They propose a method that converts textual inputs to cloze questions that contain some form of task description and processes them with a pretrained language model to map the predicted words to labels. |
| Outcome: | The proposed approach performs almost as well as hand-crafted label-to-word mappings for a number of tasks with small amounts of training data. |
Distinct Label Representations for Few-Shot Text Classification (2021.acl-short)
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| Challenge: | Existing methods for few-shot text classification ignore the semantic relevance of labels and are difficult to train because of the lack of training examples. |
| Approach: | They propose a method that generates distinct label representations that embed information specific to each label. |
| Outcome: | The proposed method significantly improves few-shot text classification across models and datasets. |
Few-Shot Learning with Siamese Networks and Label Tuning (2022.acl-long)
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| Challenge: | Recent studies have shown that few-shot text classification is a poor solution for training data-intensive tasks. |
| Approach: | They propose a method that embeds texts and labels into classifiers with proper pre-training. |
| Outcome: | The proposed approach reduces inference cost by increasing the number of labels and embeddings. |
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels (2020.emnlp-main)
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Ilias Chalkidis, Manos Fergadiotis, Sotiris Kotitsas, Prodromos Malakasiotis, Nikolaos Aletras, Ion Androutsopoulos
| Challenge: | Large-scale Multi-label Text Classification (LMTC) is a type of classification that assigns labels to a large set of labels. |
| Approach: | They propose to use probabilistic label trees to improve frequent, few and zero-shot learning . they propose to combine a new state-of-the-art method with pre-trained Transformers . |
| Outcome: | The proposed models outperform existing models on frequent, few and zero-shot learning on three datasets from different domains. |
The Benefits of Label-Description Training for Zero-Shot Text Classification (2023.emnlp-main)
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| Challenge: | Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data to classify among specific label sets in downstream tasks. |
| Approach: | They propose to use a small finetuning dataset to describe the labels for a task and to use it to further improve zero-shot accuracies. |
| Outcome: | The proposed model is more accurate than zero-shot by 17-19% absolute across topic and sentiment datasets and more robust to choices required for zero- shot classification. |
Preserving Zero-shot Capability in Supervised Fine-tuning for Multi-label Text Classification (2025.findings-naacl)
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| Challenge: | Existing methods that assume label descriptions ensure zero-shot capability lose their zero-shot capability during training. |
| Approach: | They propose a method that preserves the zero-shot capabilities of powerful dual encoders and label-wise attention networks by freezing the label encoder. |
| Outcome: | The proposed methods preserve the zero-shot capabilities of powerful dual encoder and label-wise attention network architectures by freezing the label encoder. |
Zero- and Few-Shot NLP with Pretrained Language Models (2022.acl-tutorials)
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| Challenge: | a tutorial aims to introduce NLP researchers to the latest techniques for learning from little-to-no data . aims at bringing interested researchers up to speed about the latest and ongoing techniques . |
| Approach: | They aim to introduce techniques for learning from little-to-no data using pretrained language models. |
| Outcome: | This tutorial aims to bring interested NLP researchers up to speed about recent techniques . it will cover methods from manual engineering, better inference algorithms to better tuning methods . |
Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification (2022.findings-emnlp)
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| 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. |