Yoav Artzi

6.5k total citations · 1 hit paper
32 papers, 1.7k citations indexed

About

Yoav Artzi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Yoav Artzi has authored 32 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 3 papers in Information Systems. Recurrent topics in Yoav Artzi's work include Topic Modeling (22 papers), Natural Language Processing Techniques (17 papers) and Multimodal Machine Learning Applications (13 papers). Yoav Artzi is often cited by papers focused on Topic Modeling (22 papers), Natural Language Processing Techniques (17 papers) and Multimodal Machine Learning Applications (13 papers). Yoav Artzi collaborates with scholars based in United States and Israel. Yoav Artzi's co-authors include Luke Zettlemoyer, Kilian Q. Weinberger, Felix Wu, Tianyi Zhang, Regina Barzilay, Nate Kushman, Kenton Lee, Tom Kwiatkowski, Eunsol Choi and Alane Suhr and has published in prestigious journals such as AI Magazine, Transactions of the Association for Computational Linguistics and Empirical Methods in Natural Language Processing.

In The Last Decade

Yoav Artzi

32 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

BERTScore: Evaluating Text Generation with BERT

2020 267 citations Tianyi Zhang, Felix Wu et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yoav Artzi United States	18	1.5k	566	160	52	48	32	1.7k
Hao Zhou China	19	1.6k 1.1×	540 1.0×	126 0.8×	52 1.0×	72 1.5×	84	1.9k
Jacob Andreas United States	20	1.3k 0.9×	878 1.6×	63 0.4×	36 0.7×	50 1.0×	62	1.7k
Rami Al‐Rfou United States	10	1.3k 0.9×	349 0.6×	114 0.7×	58 1.1×	46 1.0×	18	1.6k
Pankaj Kumar Singh United States	5	778 0.5×	297 0.5×	157 1.0×	57 1.1×	33 0.7×	8	1.0k
Xingcheng Yao China	2	1.1k 0.8×	322 0.6×	223 1.4×	77 1.5×	63 1.3×	3	1.4k
Zhe Zhao China	13	1.0k 0.7×	260 0.5×	140 0.9×	78 1.5×	74 1.5×	51	1.3k
Wenhu Chen United States	19	793 0.5×	344 0.6×	97 0.6×	40 0.8×	29 0.6×	67	1.1k
Shaohan Huang China	21	1.3k 0.8×	327 0.6×	252 1.6×	77 1.5×	28 0.6×	60	1.6k
Yun-Nung Chen Taiwan	22	1.4k 0.9×	261 0.5×	130 0.8×	56 1.1×	65 1.4×	93	1.6k

Countries citing papers authored by Yoav Artzi

Since Specialization

Citations

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

Fields of papers citing papers by Yoav Artzi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoav Artzi

This figure shows the co-authorship network connecting the top 25 collaborators of Yoav Artzi. A scholar is included among the top collaborators of Yoav Artzi 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 Yoav Artzi. Yoav Artzi 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:

20 of 20 papers shown

1.

Wu, Felix, Kwangyoun Kim, Shinji Watanabe, et al.. (2023). Wav2Seq: Pre-Training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages. 1–5. 19 indexed citations

2.

Gao, Ge, Hung‐Ting Chen, Yoav Artzi, & Eunsol Choi. (2023). Continually Improving Extractive QA via Human Feedback. 406–423. 3 indexed citations

3.

Suhr, Alane, et al.. (2022). Abstract Visual Reasoning with Tangram Shapes. 582–601. 9 indexed citations

4.

Wu, Felix, Kwangyoun Kim, Jing Pan, et al.. (2022). Performance-Efficiency Trade-Offs in Unsupervised Pre-Training for Speech Recognition. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 7667–7671. 16 indexed citations

5.

Zhang, Tianyi, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, & Yoav Artzi. (2021). Revisiting Few-sample BERT Fine-tuning. arXiv (Cornell University). 126 indexed citations

6.

Mehta, Harsh, Yoav Artzi, Jason Baldridge, Eugene Ie, & Piotr Mirowski. (2020). Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View. 56–62. 10 indexed citations

7.

Zhang, Tianyi, et al.. (2020). BERTScore: Evaluating Text Generation with BERT. arXiv (Cornell University). 267 indexed citations breakdown →

8.

Yu, Lili, Howard Chen, Sida I. Wang, Tao Leí, & Yoav Artzi. (2020). Interactive Classification by Asking Informative Questions. 7 indexed citations

9.

Blukis, Valts, et al.. (2019). Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight. 1415–1438. 8 indexed citations

10.

Suhr, Alane, et al.. (2019). Executing Instructions in Situated Collaborative Interactions. 2119–2130. 31 indexed citations

11.

Misra, Dipendra, et al.. (2018). Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction. 2667–2678. 71 indexed citations

12.

Suhr, Alane, et al.. (2018). Evaluating Visual Reasoning Through Grounded Language Understanding. AI Magazine. 39(2). 45–52. 1 indexed citations

13.

Suhr, Alane, et al.. (2017). A Corpus of Natural Language for Visual Reasoning. 217–223. 88 indexed citations

14.

Artzi, Yoav, Maxwell Forbes, Kenton Lee, & Maya Çakmak. (2014). Programming by Demonstration with Situated Semantic Parsing. National Conference on Artificial Intelligence. 3 indexed citations

15.

Kushman, Nate, Yoav Artzi, Luke Zettlemoyer, & Regina Barzilay. (2014). Learning to Automatically Solve Algebra Word Problems. 271–281. 208 indexed citations

16.

FitzGerald, Nicholas, Yoav Artzi, & Luke Zettlemoyer. (2013). Learning Distributions over Logical Forms for Referring Expression Generation. 1914–1925. 34 indexed citations

17.

Artzi, Yoav, Nicholas FitzGerald, & Luke Zettlemoyer. (2013). Semantic Parsing with Combinatory Categorial Grammars. Meeting of the Association for Computational Linguistics. 2–2. 11 indexed citations

18.

Kwiatkowski, Tom, Eunsol Choi, Yoav Artzi, & Luke Zettlemoyer. (2013). Scaling Semantic Parsers with On-the-Fly Ontology Matching. 1545–1556. 142 indexed citations

19.

Artzi, Yoav, Patrick Pantel, & Michael Gamon. (2012). Predicting Responses to Microblog Posts. North American Chapter of the Association for Computational Linguistics. 602–606. 61 indexed citations

20.

Artzi, Yoav & Luke Zettlemoyer. (2011). Bootstrapping Semantic Parsers from Conversations. Empirical Methods in Natural Language Processing. 421–432. 75 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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