Papers Few-Shot Text Classification

Noisy Channel Language Model Prompting for Few-Shot Text Classification

We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the…

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification. Particularly, previous studies suggest that prompt-tuning has remarkable superiority in the low-…

Distinct Label Representations for Few-Shot Text Classification

Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels th…

Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification

Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and the…

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks

First-order meta-learning algorithms have been widely used in practice to learn initial model parameters that can be quickly adapted to new tasks due to their efficiency and effectiveness. However, existing studies find …

Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System

Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled dat…

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with {``}task descriptions{''} in natural language (e.g., Radford et al., 2019). While this approach underperforms its…

Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation -- a…

A Neural Few-Shot Text Classification Reality Check

Modern classification models tend to struggle when the amount of annotated data is scarce. To overcome this issue, several neural few-shot classification models have emerged, yielding significant progress over time, both…

Uncertainty-aware Self-training for Few-shot Text Classification

Recent success of pre-trained language models crucially hinges on fine-tuning them on large amounts of labeled data for the downstream task, that are typically expensive to acquire or difficult to access for many applica…

Effective Few-Shot Classification with Transfer Learning

Few-shot learning addresses the the problem of learning based on a small amount of training data. Although more well-studied in the domain of computer vision, recent work has adapted the Amazon Review Sentiment Classific…

Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network

In this paper, we propose a new few-shot text classification method. Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled …

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to …

When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications

Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation. Many impa…

Dynamic Memory Induction Networks for Few-Shot Text Classification

This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification. The model utilizes dynamic routing to provide more flexibility to memory-based few-shot learning in order to better adapt the…

Knowledge Guided Metric Learning for Few-Shot Text Classification

The training of deep-learning-based text classification models relies heavily on a huge amount of annotation data, which is difficult to obtain. When the labeled data is scarce, models tend to struggle to achieve satisfa…

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e.g., Radford et al., 2019). While this approach underperforms its super…

Hierarchical Attention Prototypical Networks for Few-Shot Text Classification

Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, the…

When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification

Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot ta…