Elamur Research⌕
☰
home / authors / Navin Goyal

Navin Goyal

Navin Goyal has 2 papers in our library, in 2020–2021, most of them on Reasoning; often with Satwik Bhattamishra, Kabir Ahuja.

2 papers in our library · 2020–2021

OpenAlex · ORCID · Wikidata · h-index 20 · 100 works · 3,136 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 671 citations in our library · 1 more in our library without a page yet · ⭐ Paper Star rank #2223

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

🏢Microsoft ResearchBengaluru · India2009–2025 · 35 paperscurrent🏢MicrosoftRedmond · United States2008–2025 · 26 paperscurrent🏢GlaxoSmithKlinePhiladelphia · United States2011–2018 · 10 papers🎓Rutgers, The State University of New JerseyNew Brunswick · United States2004–2008 · 9 papers🎓Georgia Institute of TechnologyAtlanta · United States2008–2009 · 5 papers🎓University of FloridaGainesville · United States2008 · 3 papers🎓McGill UniversityMontreal · Canada2006–2007 · 3 papers🏢GlaxoSmithKlineLondon · United Kingdom2012–2015 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 6 Arts and Humanities · 2 works Computer Science · 2 works Artificial Intelligence · 2 works Computational Theory and Mathematics · 2 works Language and Linguistics · 2 works Mathematics · 1 work

🧭 Topics 1 Reasoning · 1 work

 Works by year

2020s

20211 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Are NLP Models really able to Solve Simple Math Word Problems? (2021 · 883 citations)
    The paper tests whether neural math word problem solvers really understand simple one-unknown arithmetic problems.
  2. On the Ability and Limitations of Transformers to Recognize Formal Languages (2020 · 109 citations)
    This EMNLP 2020 study asks which formal languages Transformers can actually learn in practice, compared with LSTMs.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Are NLP Models really able to Solve Simple Math Word Problems?Arkil Patel, Satwik Bhattamishra, Navin GoyalMova's core list · 883 citations
  2. On the Ability and Limitations of Transformers to Recognize Formal LanguagesSatwik Bhattamishra, Kabir Ahuja, Navin GoyalConference on Empirical Methods in Natural… · 109 citations
  3. Towards a Mathematics Formalisation Assistant using Large Language Models (original ↗)Ayush Agrawal et al.2022 · arXiv:2211.07524 · 19 citations · no summary yet

 Co-authors here

2 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 671 times (of the 141,840 papers whose bibliographies we read); 64 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/navin-goyal/