1 citations · 1 across the 5 of their papers we have counts for
5 papers
Self-Correction Makes LLMs Better Parsers
Ziyan Zhang, Yang Hou, Chen Gong +1
Large language models (LLMs) have achieved remarkable success across various natural language processing (NLP) tasks. However, recent studies suggest that they still face challenge…
Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation
Xuebin Wang, Lei Zhang, Zhenghua Li +3
Inspired by early research on exploring naturally annotated data for Chinese Word Segmentation (CWS), and also by recent research on integration of speech and text processing, this…
Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure
Yang Hou, Zhenghua Li
Revealing the syntactic structure of sentences in Chinese poses significant challenges for word-level parsers due to the absence of clear word boundaries. To facilitate a transitio…
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions
Houquan Zhou, Yang Hou, Zhenghua Li +4
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…
High-order Joint Constituency and Dependency Parsing
Yanggan Gu, Yang Hou, Zhefeng Wang +2
This work revisits the topic of jointly parsing constituency and dependency trees, i.e., to produce compatible constituency and dependency trees simultaneously for input sentences,…