1 code implementation • CoNLL (EMNLP) 2021 • Yang Hou, Houquan Zhou, Zhenghua Li, Yu Zhang, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
1 code implementation • 21 Feb 2025 • Houquan Zhou, Bo Zhang, Zhenghua Li, Ming Yan, Min Zhang
To address this issue, we introduce the task of General Chinese Character Error Correction (C2EC), which focuses on all three types of character errors.
no code implementations • 17 Dec 2024 • Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang
One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph.
1 code implementation • 5 Oct 2024 • Houquan Zhou, Zhenghua Li, Bo Zhang, Chen Li, Shaopeng Lai, Ji Zhang, Fei Huang, Min Zhang
This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task, which is totally different from all previous CSC approaches.
1 code implementation • 14 Nov 2023 • Houquan Zhou, Yang Hou, Zhenghua Li, Xuebin Wang, Zhefeng Wang, Xinyu Duan, Min Zhang
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition?
1 code implementation • 23 Oct 2023 • Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang
In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token.
Ranked #1 on
Grammatical Error Correction
on MuCGEC
no code implementations • 4 Jul 2022 • Houquan Zhou, Shenghua Liu, Danai Koutra, HuaWei Shen, Xueqi Cheng
Recent works try to improve scalability via graph summarization -- i. e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph.
1 code implementation • Findings (ACL) 2022 • Houquan Zhou, Yang Li, Zhenghua Li, Min Zhang
In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks.
1 code implementation • ACL 2021 • Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.
no code implementations • 19 Oct 2020 • Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, HuaWei Shen, Xueqi Cheng
As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it.
Social and Information Networks
2 code implementations • IJCAI 2020 • Yu Zhang, Houquan Zhou, Zhenghua Li
Estimating probability distribution is one of the core issues in the NLP field.
Ranked #1 on
Constituency Parsing
on CTB7
1 code implementation • 6 Mar 2020 • Houquan Zhou, Yu Zhang, Zhenghua Li, Min Zhang
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing.