no code implementations • Findings (ACL) 2022 • Tianchi Yue, Shulin Liu, Huihui Cai, Tao Yang, Shengkang Song, TingHao Yu
The generative model may bring too many changes to the original sentences and generate semantically ambiguous sentences, so it is difficult to detect grammatical errors in these generated sentences.
1 code implementation • Findings (ACL) 2022 • Shulin Liu, Shengkang Song, Tianchi Yue, Tao Yang, Huihui Cai, TingHao Yu, Shengli Sun
These methods have two limitations: (1) they have poor performance on multi-typo texts.
no code implementations • 29 Mar 2024 • Shulin Liu, Chengcheng Xu, Hao liu, TingHao Yu, Tao Yang
The recent success of Large Language Models (LLMs) has garnered significant attention in both academia and industry.
1 code implementation • ACL 2021 • Shulin Liu, Tao Yang, Tianchi Yue, Feng Zhang, Di Wang
In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors.
no code implementations • NAACL 2019 • Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou
The goal of event detection (ED) is to detect the occurrences of events and categorize them.
no code implementations • ACL 2017 • Shulin Liu, Yubo Chen, Kang Liu, Jun Zhao
This paper tackles the task of event detection (ED), which involves identifying and categorizing events.
no code implementations • ACL 2017 • Yubo Chen, Shulin Liu, Xiang Zhang, Kang Liu, Jun Zhao
Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data.