Shuoyang Ding

413 total citations
12 papers, 134 citations indexed

About

Shuoyang Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Shuoyang Ding has authored 12 papers receiving a total of 134 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Shuoyang Ding's work include Natural Language Processing Techniques (10 papers), Topic Modeling (10 papers) and Multimodal Machine Learning Applications (2 papers). Shuoyang Ding is often cited by papers focused on Natural Language Processing Techniques (10 papers), Topic Modeling (10 papers) and Multimodal Machine Learning Applications (2 papers). Shuoyang Ding collaborates with scholars based in United States, China and Greece. Shuoyang Ding's co-authors include Philipp Koehn, Kevin Duh, Hainan Xu, Adithya Renduchintala, Matt Post, Sanjeev Khudanpur, Tongfei Chen, Nanyun Peng, Hang Lv and Lei Xie and has published in prestigious journals such as Edinburgh Research Explorer and arXiv (Cornell University).

In The Last Decade

Shuoyang Ding

11 papers receiving 125 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Shuoyang Ding United States	7	127	34	29	6	3	12	134
Wenbiao Ding China	6	104 0.8×	67 2.0×	14 0.5×	7 1.2×	3 1.0×	12	139
Elizabeth Salesky United States	7	184 1.4×	32 0.9×	17 0.6×	7 1.2×	4 1.3×	20	192
Ankur Gandhe United States	8	207 1.6×	14 0.4×	54 1.9×	7 1.2×	5 1.7×	27	213
Stanislas Lauly France	3	115 0.9×	36 1.1×	12 0.4×	14 2.3×	5 1.7×	3	130
Pascale Sébillot France	7	104 0.8×	34 1.0×	14 0.5×	13 2.2×	2 0.7×	28	122
Ramon Sanabria United Kingdom	6	102 0.8×	20 0.6×	29 1.0×	2 0.3×	2 0.7×	11	116
Alexandre Bérard France	6	96 0.8×	35 1.0×	13 0.4×	5 0.8×	1 0.3×	16	111
Marco Dinarelli France	8	214 1.7×	19 0.6×	27 0.9×	5 0.8×		24	222
Ilia Kulikov United States	6	183 1.4×	46 1.4×	28 1.0×	10 1.7×	1 0.3×	12	191
Barun Patra United States	5	83 0.7×	26 0.8×	7 0.2×	5 0.8×	3 1.0×	15	106

Countries citing papers authored by Shuoyang Ding

Since Specialization

Citations

This map shows the geographic impact of Shuoyang Ding'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 Shuoyang Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuoyang Ding more than expected).

Fields of papers citing papers by Shuoyang Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shuoyang Ding. 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 Shuoyang Ding. The network helps show where Shuoyang Ding may publish in the future.

Co-authorship network of co-authors of Shuoyang Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Shuoyang Ding. A scholar is included among the top collaborators of Shuoyang Ding 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 Shuoyang Ding. Shuoyang Ding is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Żelasko, Piotr, Zhehuai Chen, Daniel Gálvez, et al.. (2025). EMMeTT: Efficient Multimodal Machine Translation Training. 1–5. 1 indexed citations

2.

Ding, Shuoyang, et al.. (2024). Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains. 488–500.

3.

Ding, Shuoyang & Philipp Koehn. (2021). Evaluating Saliency Methods for Neural Language Models. 5034–5052. 20 indexed citations

4.

Ding, Shuoyang, Adithya Renduchintala, & Kevin Duh. (2019). A Call for Prudent Choice of Subword Merge Operations.. arXiv (Cornell University). 1 indexed citations

5.

Ding, Shuoyang, Adithya Renduchintala, & Kevin Duh. (2019). A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation. 204–213. 14 indexed citations

6.

Wang, Yiming, Tongfei Chen, Xu H, et al.. (2019). Espresso: A Fast End-to-End Neural Speech Recognition Toolkit. 136–143. 37 indexed citations

7.

Ding, Shuoyang & Philipp Koehn. (2019). Parallelizable Stack Long Short-Term Memory. 1–6. 3 indexed citations

8.

Ding, Shuoyang, Hainan Xu, & Philipp Koehn. (2019). Saliency-driven Word Alignment Interpretation for Neural Machine Translation. 1–12. 27 indexed citations

9.

Post, Matt, et al.. (2019). An Exploration of Placeholding in Neural Machine Translation.. 182–192. 6 indexed citations

10.

Ding, Shuoyang, Huda Khayrallah, Philipp Koehn, et al.. (2017). The JHU Machine Translation Systems for WMT 2017. 276–282. 8 indexed citations

11.

Ding, Shuoyang, Kevin Duh, Huda Khayrallah, Philipp Koehn, & Matt Post. (2016). The JHU Machine Translation Systems for WMT 2016. Edinburgh Research Explorer. 272–280. 11 indexed citations

12.

Sun, Weiwei, et al.. (2014). Grammatical Relations in Chinese: GB-Ground Extraction and Data-Driven Parsing. 446–456. 6 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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