John Wieting

2.7k total citations
25 papers, 535 citations indexed

About

John Wieting is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, John Wieting has authored 25 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in John Wieting's work include Topic Modeling (23 papers), Natural Language Processing Techniques (22 papers) and Multimodal Machine Learning Applications (9 papers). John Wieting is often cited by papers focused on Topic Modeling (23 papers), Natural Language Processing Techniques (22 papers) and Multimodal Machine Learning Applications (9 papers). John Wieting collaborates with scholars based in United States, United Kingdom and Canada. John Wieting's co-authors include Kevin Gimpel, Karen Livescu, Mohit Bansal, Graham Neubig, Taylor Berg-Kirkpatrick, Jonathan H. Clark, Dan Garrette, Iulia Turc, Douwe Kiela and Kalpesh Krishna and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, Theory and applications of categories and arXiv (Cornell University).

In The Last Decade

John Wieting

22 papers receiving 495 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John Wieting United States	11	501	109	38	29	17	25	535
Ondřej Dušek Czechia	12	396 0.8×	66 0.6×	30 0.8×	33 1.1×	14 0.8×	62	443
Omri Abend Israel	18	755 1.5×	83 0.8×	35 0.9×	29 1.0×	7 0.4×	56	802
Rabih Zbib United States	8	508 1.0×	87 0.8×	36 0.9×	26 0.9×	19 1.1×	18	545
Luís Marujo Portugal	10	502 1.0×	77 0.7×	62 1.6×	27 0.9×	21 1.2×	15	536
Aleš Tamchyna Czechia	10	580 1.2×	124 1.1×	32 0.8×	60 2.1×	12 0.7×	29	623
Max Bartolo United Kingdom	6	398 0.8×	164 1.5×	43 1.1×	8 0.3×	13 0.8×	13	486
Ryuichi Takanobu China	11	418 0.8×	90 0.8×	67 1.8×	20 0.7×	13 0.8×	16	469
Edoardo Maria Ponti United Kingdom	12	483 1.0×	136 1.2×	21 0.6×	20 0.7×	14 0.8×	42	539
Dan Garrette United States	11	468 0.9×	115 1.1×	41 1.1×	12 0.4×	9 0.5×	23	507
Alexandre Passos United States	5	425 0.8×	45 0.4×	30 0.8×	34 1.2×	10 0.6×	12	455

Countries citing papers authored by John Wieting

Since Specialization

Citations

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

Fields of papers citing papers by John Wieting

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Wieting

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

All Works

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20 of 20 papers shown

Thakur, Nandan, Jianmo Ni, Gustavo Hernández Ábrego, et al.. (2024). Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval. 7699–7724.

Krishna, Kalpesh, et al.. (2024). PostMark: A Robust Blackbox Watermark for Large Language Models. 8969–8987. 2 indexed citations

Müller, Benjamin, John Wieting, Jonathan H. Clark, et al.. (2023). Evaluating and Modeling Attribution for Cross-Lingual Question Answering. 144–157. 1 indexed citations

Wieting, John, Jonathan H. Clark, William W. Cohen, Graham Neubig, & Taylor Berg-Kirkpatrick. (2023). Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval. 3 indexed citations

Cohen, William W., et al.. (2023). QA Is the New KR: Question-Answer Pairs as Knowledge Bases. Proceedings of the AAAI Conference on Artificial Intelligence. 37(13). 15385–15392. 2 indexed citations

Sun, Jiao, Yufei Tian, Wangchunshu Zhou, et al.. (2023). Evaluating Large Language Models on Controlled Generation Tasks. 3155–3168. 4 indexed citations

Chen, Wenhu, et al.. (2023). Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering. 1597–1610. 3 indexed citations

Wieting, John, et al.. (2023). Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense. 27469–27500.

Krishna, Kalpesh, et al.. (2022). RankGen: Improving Text Generation with Large Ranking Models. 199–232. 18 indexed citations

10.

Krishna, Kalpesh, et al.. (2022). Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature. 9882–9902. 15 indexed citations

11.

Dong, Yue, et al.. (2022). Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization. 1067–1082. 11 indexed citations

12.

Clark, Jonathan H., Dan Garrette, Iulia Turc, & John Wieting. (2022). Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation. Transactions of the Association for Computational Linguistics. 10. 73–91. 57 indexed citations

13.

Liu, Yixin, Graham Neubig, & John Wieting. (2021). On Learning Text Style Transfer with Direct Rewards. 4262–4273. 15 indexed citations

14.

Wieting, John, Taylor Berg-Kirkpatrick, Kevin Gimpel, & Graham Neubig. (2019). Beyond BLEU: Training Neural Machine Translation with Semantic Similarity. 4344–4355. 76 indexed citations

15.

Wieting, John, Kevin Gimpel, Graham Neubig, & Taylor Berg-Kirkpatrick. (2019). Simple and Effective Paraphrastic Similarity from Parallel Translations. 4602–4608. 18 indexed citations

16.

Wieting, John, et al.. (2018). Quality Signals in Generated Stories. 192–202. 9 indexed citations

17.

Wieting, John & Kevin Gimpel. (2017). Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations. arXiv (Cornell University). 8 indexed citations

18.

Wieting, John, Mohit Bansal, Kevin Gimpel, & Karen Livescu. (2016). Charagram: Embedding Words and Sentences via Character n-grams. 1504–1515. 89 indexed citations

19.

He, Hua, John Wieting, Kevin Gimpel, Jinfeng Rao, & Jimmy Lin. (2016). UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement. 1103–1108. 16 indexed citations

20.

Xiao, Cheng, Kai-Wei Chang, Mark Sammons, et al.. (2013). Illinois Cognitive Computation Group UI-CCG TAC 2013 Entity Linking and Slot Filler Validation Systems. Theory and applications of categories. 2 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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