Hi, my name is Yu Zhang ([jy tʃɑŋ], 張宇/张宇 in traditional/simplified Chinese). I am currently a third-year PhD student at HLT@SUDA, advised by Prof. Guohong Fu. I expect to graduate in 2025. Prior to this, I received my M. Eng. degree from Soochow University in 2021.
My early research focused on structured prediction tasks, specifically dependency parsing and constituency parsing.
Currently, my research interests have evolved to focus on developing efficient text generation models.
I am particularly intrigued by the prospect of developing hardware-efficient methods for linear-time sequence modeling.
As a disciple of parallel programming, I am passionate about exploring techniques that harness the power of parallel computing to develop scalable subquadratic models.
[Semantic Scholar] [Google Scholar] [DBLP] (* denotes equal contributions)
Gated Slot Attention for Efficient Linear-Time Sequence Modeling
Yu Zhang*, Songlin Yang*, Ruijie Zhu, Yue Zhang, Leyang Cui, Yiqiao Wang, Bolun Wang, Freda Shi, Bailin Wang, Wei Bi, Peng Zhou, Guohong Fu
NeurIPS 2024
Parallelizing Linear Transformers with the Delta Rule over Sequence Length
Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, Yoon Kim
NeurIPS 2024
Scalable MatMul-free Language Modeling
Ruijie Zhu, Yu Zhang, Ethan Sifferman, Tyler Sheaves, Yiqiao Wang, Dustin Richmond, Peng Zhou, Jason K. Eshraghian
Non-autoregressive Text Editing with Copy-aware Latent Alignments
Yu Zhang*, Yue Zhang*, Leyang Cui, Guohong Fu
EMNLP 2023
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Guohong Fu, Min Zhang
COLING 2022
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing
Shilin Zhou, Qingrong Xia, Zhenghua Li, Yu Zhang, Yu Hong, Min Zhang
COLING 2022 (Best Paper Award)
Fast and Accurate Neural CRF Constituency Parsing
Yu Zhang*, Houquan Zhou*, Zhenghua Li
IJCAI 2020
Efficient Second-Order TreeCRF for Neural Dependency Parsing
Yu Zhang, Zhenghua Li, Min Zhang
ACL 2020
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
Houquan Zhou*, Yu Zhang*, Zhenghua Li, Min Zhang
NLPCC 2020 (Best Paper Award)
HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
Wei Jiang, Zhenghua Li, Yu Zhang, Min Zhang
SemEval 2019
FLA: A Triton-Based Library for Hardware-Efficient Implementations of Linear Attention Mechanism
SuPar: State-of-the-art syntactic/semantic parsers
A Python package designed for structured prediction, including reproductions of many state-of-the-art syntactic/semantic parsers (with pretrained models for more than 19 languages), and highly-parallelized implementations of several well-known structured prediction algorithms.