Swabha Swayamdipta

3.2k total citations · 1 hit paper
31 papers, 1.0k citations indexed

About

Swabha Swayamdipta is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Swabha Swayamdipta has authored 31 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 2 papers in Information Systems. Recurrent topics in Swabha Swayamdipta's work include Natural Language Processing Techniques (21 papers), Topic Modeling (19 papers) and Multimodal Machine Learning Applications (4 papers). Swabha Swayamdipta is often cited by papers focused on Natural Language Processing Techniques (21 papers), Topic Modeling (19 papers) and Multimodal Machine Learning Applications (4 papers). Swabha Swayamdipta collaborates with scholars based in United States and Japan. Swabha Swayamdipta's co-authors include Noah A. Smith, Yejin Choi, Thomas Wolf, Sebastian Ruder, Matthew E. Peters, Maarten Sap, Alisa Liu, Chris Dyer, Xuhui Zhou and Archna Bhatia and has published in prestigious journals such as arXiv (Cornell University), Figshare and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

In The Last Decade

Swabha Swayamdipta

28 papers receiving 984 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.

Transfer Learning in Natural Language Processing

2019 311 citations Sebastian Ruder, Matthew E. Peters et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Swabha Swayamdipta United States	12	885	132	98	67	53	31	1.0k
Chris Tar United States	6	878 1.0×	174 1.3×	162 1.7×	93 1.4×	41 0.8×	6	1.1k
José Camacho-Collados United Kingdom	20	1.3k 1.5×	144 1.1×	113 1.2×	47 0.7×	73 1.4×	72	1.4k
Carina Silberer Germany	11	1.1k 1.3×	243 1.8×	180 1.8×	65 1.0×	108 2.0×	19	1.4k
Steve Yuan United States	4	852 1.0×	152 1.2×	164 1.7×	96 1.4×	41 0.8×	4	1.1k
Maciej Piasecki Poland	15	796 0.9×	50 0.4×	98 1.0×	74 1.1×	40 0.8×	115	1.1k
Luis Espinosa-Anke United Kingdom	17	663 0.7×	65 0.5×	53 0.5×	88 1.3×	53 1.0×	66	867
John Pavlopoulos Greece	13	1.5k 1.7×	125 0.9×	218 2.2×	75 1.1×	20 0.4×	42	1.8k
Anatole Gershman United States	14	545 0.6×	108 0.8×	153 1.6×	57 0.9×	18 0.3×	48	842
Amir Pouran Ben Veyseh United States	10	616 0.7×	75 0.6×	160 1.6×	81 1.2×	56 1.1×	23	936
Shashi Narayan United Kingdom	14	1.3k 1.5×	174 1.3×	113 1.2×	38 0.6×	86 1.6×	32	1.5k

Countries citing papers authored by Swabha Swayamdipta

Since Specialization

Citations

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

Fields of papers citing papers by Swabha Swayamdipta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Swabha Swayamdipta

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

Kulkarni, Atharva, et al.. (2025). Evaluating Evaluation Metrics – The Mirage of Hallucination Detection. 19013–19032.

2.

Swayamdipta, Swabha, et al.. (2025). ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations. 25466–25499. 1 indexed citations

3.

Choi, Yejin, et al.. (2024). NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge. 4502–4520. 1 indexed citations

4.

Swayamdipta, Swabha, et al.. (2024). Compare without Despair: Reliable Preference Evaluation with Generation Separability. 12787–12805. 1 indexed citations

5.

Dong, Xingjian, et al.. (2024). Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression. 12943–12959. 1 indexed citations

6.

Swayamdipta, Swabha, et al.. (2024). Annotating FrameNet via Structure-Conditioned Language Generation. 681–692. 2 indexed citations

7.

Chen, Hanjie, et al.. (2023). REV: Information-Theoretic Evaluation of Free-Text Rationales. 2007–2030. 10 indexed citations

8.

Liu, Alisa, Zhaofeng Wu, Julian Michael, et al.. (2023). We’re Afraid Language Models Aren’t Modeling Ambiguity. 790–807. 18 indexed citations

9.

Zhou, Xuhui, Hao Zhu, Thomas Davidson, et al.. (2023). COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements. 6294–6315. 9 indexed citations

10.

Choi, Yejin, et al.. (2022). NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation. 5056–5072. 10 indexed citations

11.

Sap, Maarten, et al.. (2022). Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5884–5906. 91 indexed citations

12.

Liu, Alisa, Swabha Swayamdipta, Noah A. Smith, & Yejin Choi. (2022). WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation. 6826–6847. 82 indexed citations

13.

Swayamdipta, Swabha, et al.. (2021). MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation.. arXiv (Cornell University). 1 indexed citations

14.

Liu, Alisa, Maarten Sap, Ximing Lu, et al.. (2021). DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts. 6691–6706. 94 indexed citations

15.

Liu, Alisa, Maarten Sap, Ximing Lu, et al.. (2021). On-the-Fly Controlled Text Generation with Experts and Anti-Experts.. arXiv (Cornell University). 5 indexed citations

16.

Zhou, Xuhui, Maarten Sap, Swabha Swayamdipta, Yejin Choi, & Noah A. Smith. (2021). Challenges in Automated Debiasing for Toxic Language Detection. 3143–3155. 68 indexed citations

17.

Malaviya, Chaitanya, Swabha Swayamdipta, Ronan Le Bras, et al.. (2020). G-DAUG: Generative Data Augmentation for Commonsense Reasoning. arXiv (Cornell University). 5 indexed citations

18.

Ruder, Sebastian, Matthew E. Peters, Swabha Swayamdipta, & Thomas Wolf. (2019). Transfer Learning in Natural Language Processing. 15–18. 311 indexed citations breakdown →

19.

Baker, Collin F., Michael Ellsworth, Miriam R. L. Petruck, & Swabha Swayamdipta. (2018). Frame Semantics across Languages: Towards a Multilingual FrameNet. International Conference on Computational Linguistics. 9–12. 2 indexed citations

20.

Thomson, Sam, Brendan O’Connor, Jeffrey Flanigan, et al.. (2014). CMU: Arc-Factored, Discriminative Semantic Dependency Parsing. Figshare. 176–180. 8 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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