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.
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
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
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.
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.
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 • 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
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 • 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).