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20162023
most citedERNIE: Enhanced Language Representation with Informative Entities

135 citations · 1.4k across the 127 of their papers we have counts for

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Showing 2021 · cs.CLShow all

33 papers · 2 filters

cs.CL2021★ 10 cited

YACLC: A Chinese Learner Corpus with Multidimensional Annotation

Yingying Wang, Cunliang Kong, Liner Yang +8

Learner corpus collects language data produced by L2 learners, that is second or foreign-language learners. This resource is of great relevance for second language acquisition rese…

cs.CL2021★ 7 cited

CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark

Yuan Yao, Qingxiu Dong, Jian Guan +32

Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding ro…

cs.CL2021★ 65 cited

OpenPrompt: An Open-source Framework for Prompt-learning

Ning Ding, Shengding Hu, Weilin Zhao +4

Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to -style prediction, autoregressiv…

cs.CL2021★ 57 cited

On Transferability of Prompt Tuning for Natural Language Processing

Yusheng Su, Xiaozhi Wang, Yujia Qin +10

Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-paramet…

cs.CL2021★ 3 cited

Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer

Fanchao Qi, Yangyi Chen, Xurui Zhang +3

Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation…

cs.CL2021★ 7 cited

Exploring Universal Intrinsic Task Subspace via Prompt Tuning

Yujia Qin, Xiaozhi Wang, Yusheng Su +10

Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find…