ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing models for text classification are not universally applicable and lack annotated data. |
| Approach: | They propose a framework for universal zero and few shot classification with supervised contrastive pretraining that can generalize to diverse classification tasks in both zero and many shot settings. |
| Outcome: | The proposed framework outperforms baseline models in zero and few shot settings. |
Similar Papers
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start (2020.emnlp-main)
Copied to clipboard
| Challenge: | a current approach to solving NLP problems is to build a problem-specific dataset . current approaches do not allow for transforming tasks into textual entailment . |
| Approach: | They propose a pretrained textual entailment system that can generalize across domains . they argue that when is it worth transforming an NLP task into textual detailment? |
| Outcome: | The proposed model can generalize across domains with few examples, the authors argue . they show that it can be used for several downstream NLP tasks with limited annotations . |
A weakly supervised textual entailment approach to zero-shot text classification (2023.eacl-main)
Copied to clipboard
Marc Pàmies, Joan Llop, Francesco Multari, Nicolau Duran-Silva, César Parra-Rojas, Aitor Gonzalez-Agirre, Francesco Alessandro Massucci, Marta Villegas
| Challenge: | Existing methods to train on weakly supervised datasets are expensive due to the computational cost of pre-training. |
| Approach: | They propose a method that trains on a weakly supervised dataset that is used as a proxy for a textual entailment problem and a target zero-shot text classification task. |
| Outcome: | The proposed model achieves state-of-the-art performance in the scientific domain and competitive results in other areas. |
Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)
Copied to clipboard
| Challenge: | Pre-trained BERT models with no fine-tuning can yield competitive performance against BERT fine- tuned for NLI. |
| Approach: | They propose to use any target label into a sentence of hypothesis and verify whether it could be entailed by the input. |
| Outcome: | The proposed models perform better than models fine-tuned for BERT, but the results are in general negative. |
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach (D19-1)
Copied to clipboard
| Challenge: | 0Shot-TC is a challenging NLU problem to which little attention has been paid by the research community. |
| Approach: | They propose to use a standardized evaluation system to classify text snippets without seeing task specific training data. |
| Outcome: | The proposed model is based on a set of standardized evaluations and state-of-the-art baselines. |
Label Agnostic Pre-training for Zero-shot Text Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to text classification assume a fixed set of labels . however, in real-world applications, there exists an infinite label space for describing a given text . |
| Approach: | They propose two new methods that inject aspect-level understanding into pre-trained models at train time to improve zero-shot generalization. |
| Outcome: | The proposed methods improve zero-shot generalization on a set of challenging datasets. |
EDEntail: An Entailment-based Few-shot Text Classification with Extensional Definition (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for few-shot text classification use either class labels or intensional definitions of class labels for label semantics expression. |
| Approach: | They propose a method that employs extensional definition of class labels in hypotheses and then order and format them into a sequence to form hypothese . |
| Outcome: | The proposed method surpasses supervised-learning methods and prompt-based methods on five classification datasets and is comparable to state-of-the-art models. |
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition (2022.coling-1)
Copied to clipboard
| Challenge: | Existing few-shot named entity recognition methods focus on leveraging existing datasets in the rich-resource domains which might fail in training-from-scratch setting. |
| Approach: | They propose a multi-task learning framework for Few-shot named entity recognition without using source domain data. |
| Outcome: | The proposed framework outperforms state-of-the-art few-shot named entity recognition methods on a training-from-scratch dataset. |
Zero- and Few-Shot NLP with Pretrained Language Models (2022.acl-tutorials)
Copied to clipboard
| 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 . |
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021.naacl-main)
Copied to clipboard
| Challenge: | Text classification is usually studied by labeling texts with relevant categories from a predefined set. |
| Approach: | They propose a task where a system incrementally handles multiple rounds of new classes . they propose two entailment approaches, ENTAILMENT and HYBRID, which show promise . |
| Outcome: | The proposed task is based on a few-shot text classification task in the NLP domain. |
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets (2025.acl-srw)
Copied to clipboard
| Challenge: | Recent advances in NLP have enabled the use of text-to-text annotation without providing training samples. |
| Approach: | They propose a text-to-text interface for automatic annotation using written guidelines without providing training samples. |
| Outcome: | The proposed approach is comparable with the fine-tuned BERT but without any training data. |