28 citations · 51 across the 2 of their papers we have counts for
5 papers
FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
Liang Xu, Xiaojing Lu, Chenyang Yuan +8
Pretrained Language Models (PLMs) have achieved tremendous success in natural language understanding tasks. While different learning schemes -- fine-tuning, zero-shot, and few-shot…
OCNLI: Original Chinese Natural Language Inference
Hai Hu, Kyle Richardson, Liang Xu +3
Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, mo…
CLUE: A Chinese Language Understanding Evaluation Benchmark
Liang Xu, Hai Hu, Xuanwei Zhang +29
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These com…
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
Hai Hu, Qi Chen, Kyle Richardson +3
We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to exi…
Probing Natural Language Inference Models through Semantic Fragments
Kyle Richardson, Hai Hu, Lawrence S. Moss +1
Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, an…