most citedVision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment

7 citations · 20 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CL20232 cited

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters

Yinghui Li, Zishan Xu, Shaoshen Chen +7

Writing assistance is an application closely related to human life and is also a fundamental Natural Language Processing (NLP) research field. Its aim is to improve the correctness…

cs.CL2023

MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction

Jingheng Ye, Yinghui Li, Yangning Li +1

Data Augmentation through generating pseudo data has been proven effective in mitigating the challenge of data scarcity in the field of Grammatical Error Correction (GEC). Various…

cs.CL20231 cited

A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check

Haojing Huang, Jingheng Ye, Qingyu Zhou +4

In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task…

cs.CL2023

Retrieval-Augmented Meta Learning for Low-Resource Text Classification

Rongsheng Li, Yangning Li, Yinghui Li +4

Meta learning have achieved promising performance in low-resource text classification which aims to identify target classes with knowledge transferred from source classes with sets…

cs.CL2023

Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection

Chaiyut Luoyiching, Yangning Li, Yinghui Li +4

Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic because it needs to categorize both seen and novel intents simultaneously. Previous GFSID methods rely on…

cs.CL20233 cited

SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding

Tianyu Yu, Chengyue Jiang, Chao Lou +12

Large language models (LLMs) have shown impressive ability for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which…