Su Zhu

1.1k total citations
32 papers, 535 citations indexed

About

Su Zhu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Su Zhu has authored 32 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 1 paper in Information Systems. Recurrent topics in Su Zhu's work include Topic Modeling (27 papers), Speech and dialogue systems (20 papers) and Natural Language Processing Techniques (17 papers). Su Zhu is often cited by papers focused on Topic Modeling (27 papers), Speech and dialogue systems (20 papers) and Natural Language Processing Techniques (17 papers). Su Zhu collaborates with scholars based in China, United States and Canada. Su Zhu's co-authors include Kai Yu, Yanbin Zhao, Ruisheng Cao, Kai Sun, Zhi Chen, Lu Chen, Zijian Zhao, Lu Chen, Lu Chen and Lu Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE/ACM Transactions on Audio Speech and Language Processing and Transactions of the Association for Computational Linguistics.

In The Last Decade

Su Zhu

30 papers receiving 510 citations

Peers

Su Zhu
S. R. K. Branavan United States
Jingtao Zhan China
Xiangyang Zhou China
Dan Garrette United States
Samuel Weinbach United States
Muyun Yang China
Yuxian Gu China
Myung-Gil Jang South Korea
Wenpeng Hu China
Daniel Beck Australia
S. R. K. Branavan United States
Su Zhu
Citations per year, relative to Su Zhu Su Zhu (= 1×) peers S. R. K. Branavan

Countries citing papers authored by Su Zhu

Since Specialization
Citations

This map shows the geographic impact of Su Zhu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Su Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Su Zhu more than expected).

Fields of papers citing papers by Su Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Su Zhu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Su Zhu. The network helps show where Su Zhu may publish in the future.

Co-authorship network of co-authors of Su Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Su Zhu. A scholar is included among the top collaborators of Su Zhu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Su Zhu. Su Zhu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ma, Da, Lu Chen, Bei Chen, et al.. (2025). DFM: Dialogue foundation model for universal large-scale dialogue-oriented task learning. SHILAP Revista de lepidopterología. 6. 108–117.
2.
Zhu, Su, et al.. (2023). SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling. 668–675. 2 indexed citations
3.
Chen, Zhi, et al.. (2023). OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue. Transactions of the Association for Computational Linguistics. 11. 68–84. 5 indexed citations
4.
Cao, Ruisheng, Lu Chen, Zhi Chen, et al.. (2021). LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations. 2541–2555. 67 indexed citations
5.
Chen, Zhi, Lu Chen, Yanbin Zhao, et al.. (2021). ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser. 5567–5577. 30 indexed citations
6.
Zhu, Su, Jieyu Li, Lu Chen, & Kai Yu. (2020). Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking. 766–781. 38 indexed citations
7.
Chen, Lu, et al.. (2020). Neural Graph Matching Networks for Chinese Short Text Matching. 6152–6158. 27 indexed citations
8.
Chen, Lu, et al.. (2020). Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 7521–7528. 86 indexed citations
9.
Cao, Ruisheng, Su Zhu, Chenyu Yang, et al.. (2020). Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing. 6806–6817. 21 indexed citations
10.
11.
Zhao, Zijian, Su Zhu, & Kai Yu. (2019). Data Augmentation with Atomic Templates for Spoken Language Understanding. 3635–3641. 18 indexed citations
12.
Zhao, Zijian, Su Zhu, & Kai Yu. (2019). A Hierarchical Decoding Model for Spoken Language Understanding from Unaligned Data. 7305–7309. 8 indexed citations
13.
Zhu, Su & Kai Yu. (2018). Concept Transfer Learning for Adaptive Language Understanding. 391–399. 10 indexed citations
14.
15.
Sun, Kai, et al.. (2016). Hybrid Dialogue State Tracking for Real World Human-to-Human Dialogues. 2060–2064. 3 indexed citations
16.
Zhu, Su, et al.. (2016). Rich punctuations prediction using large-scale deep learning. 8. 1–5. 3 indexed citations
17.
Xie, Qizhe, et al.. (2015). Recurrent Polynomial Network for Dialogue State Tracking with Mismatched Semantic Parsers. 295–304. 5 indexed citations
18.
Zhu, Su, et al.. (2014). Semantic parser enhancement for dialogue domain extension with little data. 336–341. 14 indexed citations
19.
Sun, Kai, Lu Chen, Su Zhu, & Kai Yu. (2014). A generalized rule based tracker for dialogue state tracking. 1. 330–335. 29 indexed citations
20.
Sun, Kai, Lu Chen, Su Zhu, & Kai Yu. (2014). The SJTU System for Dialog State Tracking Challenge 2. 30 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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