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Nicholas Lourie

Nicholas Lourie has 2 papers in our library, in 2019–2020, most of them on Question answering, Reasoning; often with Alon Talmor, Jonathan Berant, Jonathan Herzig.

2 papers in our library · 2019–2020

OpenAlex · h-index 12 · 29 works · 2,222 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,201 times in our library · ⭐ Paper Stars ranking: #1450

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

🎓New York UniversityNew York · United States2021–2026 · 9 paperscurrent🔬Allen Institute for Artificial IntelligenceSeattle · United States2018–2021 · 9 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Question answering · 1 workReasoning · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Knowledge representation and reasoning · 1 work

 Works by year

2020s

20201 paperLearning from Task Descriptions

2010s

20191 paperCommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

 Papers

The most cited first (OpenAlex and our library).

  1. CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (2019 · 1,639 citations)
    The authors aim to determine whether systems can answer questions that require general knowledge about the world rather than finding the answer in a given text.
  2. Learning from Task Descriptions (2020 · 111 citations)
    Most NLP systems learn a new task only after seeing thousands of labeled examples. Humans can often do the same task just by reading instructions. The authors propose "learning from task descriptions" as a general framework for this. A model receives both an input and a natural-language description of the task, and…

 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 1,201 times (of the 141,840 papers whose bibliographies we read); 147 citing papers with a page here →.

Updated

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