Yiwen Zhang

TL;DR: The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is introduced, an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text.

TL;DR: This paper introduced CLUE, a Chinese language understanding evaluation benchmark, which includes eight different tasks, including single-sentence classification, sentence pair classification, and machine reading comprehension. But the CLUE benchmark is limited to Chinese only.

TL;DR: The IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese is described, with ensemble models achieving the highest F1 score in simplified Chinese track and the second highest in traditional Chinese track.

TL;DR: This article presented ArguGPT, a balanced corpus of 4,038 argumentative essays generated by 7 GPT models in response to essay prompts from three sources: (1) in-class or homework exercises, (2) TOEFL and (3) GRE writing tasks.

TL;DR: A novel Learn to Adapt network using a variant meta-learning framework that is capable of representing all prototypes and samples from both classes to a more consistent distribution in a global space and outperforms several competitive previous approaches by large margins.