Yonatan Bisk

3.9k total citations · 1 hit paper
46 papers, 1.2k citations indexed

About

Yonatan Bisk is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Yonatan Bisk has authored 46 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 21 papers in Computer Vision and Pattern Recognition and 3 papers in Information Systems. Recurrent topics in Yonatan Bisk's work include Topic Modeling (29 papers), Natural Language Processing Techniques (27 papers) and Multimodal Machine Learning Applications (18 papers). Yonatan Bisk is often cited by papers focused on Topic Modeling (29 papers), Natural Language Processing Techniques (27 papers) and Multimodal Machine Learning Applications (18 papers). Yonatan Bisk collaborates with scholars based in United States, United Kingdom and Netherlands. Yonatan Bisk's co-authors include Yejin Choi, Ari Holtzman, Yonatan Belinkov, Ali Farhadi, Rowan Zellers, Jianfeng Gao, Jianwei Yang, Julia Hockenmaier, Daniel Marcu and Deniz Yüret and has published in prestigious journals such as IEEE Robotics and Automation Letters, Transactions of the Association for Computational Linguistics and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Yonatan Bisk

45 papers receiving 1.1k 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.

HellaSwag: Can a Machine Really Finish Your Sentence?

2019 332 citations Rowan Zellers, Ari Holtzman et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yonatan Bisk United States	17	987	600	68	34	32	46	1.2k
Jinpeng Wang China	17	502 0.5×	457 0.8×	65 1.0×	33 1.0×	17 0.5×	58	810
Elena Voita Netherlands	6	727 0.7×	352 0.6×	51 0.8×	47 1.4×	11 0.3×	13	913
Marjan Ghazvininejad United States	15	1.1k 1.1×	421 0.7×	78 1.1×	51 1.5×	17 0.5×	32	1.3k
Yun-Nung Chen Taiwan	22	1.4k 1.4×	261 0.4×	130 1.9×	56 1.6×	22 0.7×	93	1.6k
Sainbayar Sukhbaatar United States	7	511 0.5×	276 0.5×	52 0.8×	35 1.0×	18 0.6×	15	634
Yoav Artzi United States	18	1.5k 1.5×	566 0.9×	160 2.4×	52 1.5×	44 1.4×	32	1.7k
Felix Wu United States	13	900 0.9×	396 0.7×	76 1.1×	87 2.6×	10 0.3×	19	1.1k
Yichun Yin China	10	907 0.9×	365 0.6×	99 1.5×	61 1.8×	16 0.5×	18	1.1k
Dinghan Shen United States	15	933 0.9×	474 0.8×	106 1.6×	40 1.2×	25 0.8×	24	1.2k
David Talbot United Kingdom	12	1000 1.0×	282 0.5×	78 1.1×	57 1.7×	10 0.3×	18	1.2k

Countries citing papers authored by Yonatan Bisk

Since Specialization

Citations

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

Fields of papers citing papers by Yonatan Bisk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yonatan Bisk

This figure shows the co-authorship network connecting the top 25 collaborators of Yonatan Bisk. A scholar is included among the top collaborators of Yonatan Bisk 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 Yonatan Bisk. Yonatan Bisk 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.

Bisk, Yonatan, et al.. (2024). Tools Fail: Detecting Silent Errors in Faulty Tools. 14272–14289. 1 indexed citations

2.

Francis, Jonathan, et al.. (2024). MOSAIC: Learning Unified Multi-Sensory Object Property Representations for Robot Learning via Interactive Perception. 15381–15387. 2 indexed citations

3.

Wang, Ruiyi, Haofei Yu, Wenxin Zhang, et al.. (2024). SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents. 12912–12940. 1 indexed citations

4.

Lee, Jae-Young, Ximing Lu, Jack Hessel, et al.. (2024). How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models. 13060–13077. 1 indexed citations

5.

Bisk, Yonatan, et al.. (2023). The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment. 1588–1600. 5 indexed citations

6.

Tsai, Yao-Hung Hubert, Wei Ding, Ali Farhadi, et al.. (2023). Self-Supervised Object Goal Navigation with In-Situ Finetuning. 7119–7126.

7.

Gui, Liangke, Borui Wang, Qiuyuan Huang, et al.. (2022). KAT: A Knowledge Augmented Transformer for Vision-and-Language. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 956–968. 67 indexed citations

8.

Pagnoni, Artidoro, et al.. (2022). EvEntS ReaLM: Event Reasoning of Entity States via Language Models. 1982–1997. 1 indexed citations

9.

Zhu, Hao, et al.. (2022). Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue. 9361–9368. 4 indexed citations

10.

Palaskar, Shruti, et al.. (2022). On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization. 2644–2657. 2 indexed citations

11.

Chen, Kezhen, Qiuyuan Huang, Yonatan Bisk, Daniel McDuff, & Jianfeng Gao. (2021). KB-VLP: Knowledge Based Vision and Language Pretraining. International Conference on Machine Learning. 3 indexed citations

12.

Chandu, Khyathi Raghavi, Yonatan Bisk, & Alan W. Black. (2021). Grounding ‘Grounding’ in NLP. 4283–4305. 18 indexed citations

13.

Thomason, Jesse, Daniel Gordon, & Yonatan Bisk. (2019). Shifting the Baseline: Single Modality Performance on Visual Navigation &. 1977–1983. 26 indexed citations

14.

Zellers, Rowan, Ari Holtzman, Yonatan Bisk, Ali Farhadi, & Yejin Choi. (2019). HellaSwag: Can a Machine Really Finish Your Sentence?. 4791–4800. 332 indexed citations breakdown →

15.

Li, Xiujun, Chunyuan Li, Qiaolin Xia, et al.. (2019). Robust Navigation with Language Pretraining and Stochastic Sampling. 1494–1499. 61 indexed citations

16.

Belinkov, Yonatan & Yonatan Bisk. (2018). Synthetic and Natural Noise Both Break Neural Machine Translation. International Conference on Learning Representations. 144 indexed citations

17.

Tran, Ke, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, & Kevin Knight. (2016). Unsupervised Neural Hidden Markov Models. UvA-DARE (University of Amsterdam). 63–71. 25 indexed citations

18.

Bisk, Yonatan, Daniel Marcu, & William Wong. (2016). Towards a Dataset for Human Computer Communication via Grounded Language Acquisition. National Conference on Artificial Intelligence. 12 indexed citations

19.

Bisk, Yonatan & Julia Hockenmaier. (2012). Induction of Linguistic Structure with Combinatory Categorial Grammars. North American Chapter of the Association for Computational Linguistics. 90–95. 4 indexed citations

20.

Hockenmaier, Julia & Yonatan Bisk. (2010). Normal-form parsing for Combinatory Categorial Grammars with generalized composition and type-raising. International Conference on Computational Linguistics. 465–473. 10 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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