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Pararth Shah

Pararth Shah has 3 papers in our library, in 2018–2019, most of them on Dialogue systems, Reasoning, Knowledge graphs & ontologies; often with Bing Liu, Dilek Hakkani-Tür, Gokhan Tur.

3 papers in our library · 2018–2019

OpenAlex · h-index 14 · 32 works · 1,969 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 2,415 citations in our library · ⭐ Paper Star rank #2477

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢GoogleMountain View · United States2017–2023 · 8 papers🏢MetaMenlo Park · United States2018–2020 · 5 papers🏢MetaLondon · United Kingdom2019–2020 · 2 papers🎓Carnegie Mellon UniversityPittsburgh · United States2019 · 2 papers🎓Indian Institute of Technology BombayMumbai · India2015–2016 · 2 papers🎓Stanford UniversityStanford · United States2014–2015 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4 Artificial Intelligence · 3 works Arts and Humanities · 3 works Computer Science · 3 works Language and Linguistics · 3 works

🧭 Topics 3 Dialogue systems · 3 works Knowledge graphs & ontologies · 1 work Reasoning · 1 work

 Works by year

2010s

20191 paper20182 papers

 Papers

The most cited first (OpenAlex and our library).

  1. OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (2019 · 448 citations)
    Open-domain dialog agents must decide which related entities to bring up next. A large knowledge graph offers far too many factually valid options, and most would feel unnatural in conversation. The authors treat choosing the next entity as learning walks over a KG conditioned on dialog context. They build…
  2. Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (2018 · 212 citations)
    Task-oriented dialogue agents are often pre-trained with supervised learning on dialogue corpora and then refined with reinforcement learning (RL) from user feedback.
  3. Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (2018 · 181 citations)
    The paper addresses a practical obstacle for task-oriented conversational agents. End-to-end neural models need large annotated dialogue corpora, which are costly to build for every new domain. Wizard-of-Oz collection also often misses dialogue flows a developer expects the agent to handle.

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 2,415 times (of the 141,840 papers whose bibliographies we read); 184 citing papers with a page here →.

Updated

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