FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark

Abstract

Pretrained Language Models (PLMs) have achieved tremendous success in natural language understanding tasks. While different learning schemes -- fine-tuning, zero-shot, and few-shot learning -- have been widely explored and compared for languages such as English, there is comparatively little work in Chinese to fairly and comprehensively evaluate and compare these methods and thus hinders cumulative progress. In this paper, we introduce the Chinese Few-shot Learning Evaluation Benchmark (FewCLUE), the first comprehensive few-shot evaluation benchmark in Chinese. It includes nine tasks, ranging from single-sentence and sentence-pair classification tasks to machine reading comprehension tasks. We systematically evaluate five state-of-the-art (SOTA) few-shot learning methods (including PET, ADAPET, LM-BFF, P-tuning and EFL), and compare their performance with fine-tuning and zero-shot learning schemes on the newly constructed FewCLUE benchmark. Experimental results reveal that: 1) The effect of different few-shot learning methods is sensitive to the pre-trained model to which the methods are applied; 2) PET and P-tuning achieve the best overall performance with RoBERTa and ERNIE respectively. Our benchmark is used in the few-shot learning contest of NLPCC 2021. In addition, we provide a user-friendly toolkit, as well as an online leaderboard to help facilitate further progress on Chinese few-shot learning. We provide a baseline performance on different learning methods, a reference for future research.

10 pages, 3 tables

References in corpus (8)

• Language Models are Few-Shot Learners
• ERNIE: Enhanced Representation through Knowledge Integration
• Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping
• Revisiting Few-sample BERT Fine-tuning
• A Closer Look at Few-shot Classification
• Entailment as Few-Shot Learner
• NEZHA: Neural Contextualized Representation for Chinese Language Understanding
• OCNLI: Original Chinese Natural Language Inference

Cited by in corpus (5)

• P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
• AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
• NSP-BERT: A Prompt-based Few-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction
• Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
• TAPE: Assessing Few-shot Russian Language Understanding