Pararth Shah

2.0k total citations
13 papers, 376 citations indexed

About

Pararth Shah is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pararth Shah has authored 13 papers receiving a total of 376 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 2 papers in Information Systems and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pararth Shah's work include Topic Modeling (12 papers), Speech and dialogue systems (7 papers) and Natural Language Processing Techniques (7 papers). Pararth Shah is often cited by papers focused on Topic Modeling (12 papers), Speech and dialogue systems (7 papers) and Natural Language Processing Techniques (7 papers). Pararth Shah collaborates with scholars based in United States, India and Israel. Pararth Shah's co-authors include Seungwhan Moon, Rajen Subba, Anuj Kumar, Dilek Hakkani‐Tür, Gökhan Tür, Bing Liu, Dongyeop Kang, Anusha Balakrishnan, Paul Crook and Y-Lan Boureau and has published in prestigious journals such as .

In The Last Decade

Pararth Shah

13 papers receiving 358 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pararth Shah United States	8	356	100	90	11	9	13	376
Xiangyang Zhou China	7	473 1.3×	56 0.6×	67 0.7×	10 0.9×	12 1.3×	9	502
Rajen Subba United States	9	366 1.0×	58 0.6×	83 0.9×	8 0.7×	8 0.9×	18	396
Rotem Dror Israel	9	366 1.0×	51 0.5×	57 0.6×	9 0.8×	18 2.0×	13	427
Pamela Forner Spain	11	270 0.8×	63 0.6×	44 0.5×	8 0.7×	13 1.4×	18	319
Jiwei Tan China	10	364 1.0×	92 0.9×	78 0.9×	20 1.8×	14 1.6×	14	410
Xiaoxue Zang China	7	152 0.4×	67 0.7×	54 0.6×	6 0.5×	13 1.4×	23	223
Quoc-Tuan Truong Singapore	7	185 0.5×	105 1.1×	83 0.9×	10 0.9×	12 1.3×	11	250
Ondřej Dušek Czechia	12	396 1.1×	30 0.3×	66 0.7×	18 1.6×	7 0.8×	62	443
Rahul Bhagat United States	9	380 1.1×	55 0.6×	35 0.4×	7 0.6×	21 2.3×	14	412
Ryohei Sasano Japan	11	282 0.8×	42 0.4×	48 0.5×	6 0.5×	11 1.2×	50	320

Countries citing papers authored by Pararth Shah

Since Specialization

Citations

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

Fields of papers citing papers by Pararth Shah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pararth Shah

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

13 of 13 papers shown

1.

Goel, Rahul, Waleed Ammar, Max W. Chang, et al.. (2023). PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs. 10820–10833. 5 indexed citations

2.

Shah, Pararth, et al.. (2020). Multi-Action Dialog Policy Learning with Interactive Human Teaching. 290–296. 8 indexed citations

3.

Xu, Hu, Seungwhan Moon, Honglei Liu, et al.. (2020). User Memory Reasoning for Conversational Recommendation. 5288–5308. 26 indexed citations

4.

Zhou, Zhenpeng, Ahmad Beirami, Paul Crook, et al.. (2020). Resource Constrained Dialog Policy Learning Via Differentiable Inductive Logic Programming. 6775–6787. 1 indexed citations

5.

Moon, Seungwhan, Pararth Shah, Anuj Kumar, & Rajen Subba. (2019). OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs. 845–854. 176 indexed citations

6.

Kang, Dongyeop, Anusha Balakrishnan, Pararth Shah, et al.. (2019). Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue. 1951–1961. 54 indexed citations

7.

Moon, Seungwhan, Pararth Shah, Anuj Kumar, & Rajen Subba. (2019). Memory Graph Networks for Explainable Memory-grounded Question Answering. 728–736. 8 indexed citations

8.

Moon, Seungwhan, Pararth Shah, Rajen Subba, & Anuj Kumar. (2019). Memory Grounded Conversational Reasoning. 145–150. 2 indexed citations

9.

Shah, Pararth, Dilek Hakkani‐Tür, Bing Liu, & Gökhan Tür. (2018). Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning. 41–51. 53 indexed citations

10.

Gür, İzzeddin, Dilek Hakkani‐Tür, Gökhan Tür, & Pararth Shah. (2018). User Modeling for Task Oriented Dialogues. 16 indexed citations

11.

Shah, Pararth, et al.. (2016). Learning to Collectively Link Entities. 1–9. 1 indexed citations

12.

Shah, Pararth, Dilek Hakkani‐Tür, & Larry Heck. (2016). Interactive reinforcement learning for task-oriented dialogue management. 22 indexed citations

13.

Shah, Pararth, et al.. (2014). System for collective entity disambiguation. 111–118. 4 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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