---
title: "Matt Gardner — papers and cards"
description: "Matt Gardner: 4 paper cards in the Research library (2019–2020), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/matt-gardner/"
---
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# Matt Gardner

4 paper cards in the Research library · 2019–2020

Matt Gardner wrote 4 of the papers Mova reads, in 2019–2020; often with [Pradeep Dasigi](https://md.research.elamur.ai/free/en/authors/pradeep-dasigi/), [Dheeru Dua](https://md.research.elamur.ai/free/en/authors/dheeru-dua/), [Nelson F. Liu](https://md.research.elamur.ai/free/en/authors/nelson-f-liu/).

[OpenAlex](https://openalex.org/A5035088083) · [ORCID](https://orcid.org/0000-0001-8458-1727) · [**h-index 42**](https://openalex.org/authors/A5035088083) · [20076 citations](https://openalex.org/authors/A5035088083) · [99 works](https://openalex.org/works?filter=author.id:A5035088083) — openalex-snapshot, 2026-10-14

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

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Matt+Gardner) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Matt+Gardner) · [dblp](https://dblp.org/search?q=Matt+Gardner) · [ACL Anthology](https://aclanthology.org/search/?q=Matt+Gardner) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Matt+Gardner)

## Ideas Mova took (1)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

- **kept** — Keep events in non-real modes out of «what happened» answers: negated frames (neg), conditional ones (cond) and the unrealised complements proposed in white-2016-universal-decompositional-semantics-universal-dependencies I1 do not answer «What did the lion… *([TORQUE: A Reading Comprehension Dataset of Temporal…](https://md.research.elamur.ai/free/en/papers/ning-2020-torque-reading-comprehension-dataset-temporal/) · an «if» clause is irrealis for the student (PRAG_OFF=cond-irrealis): 7702/2200→7704/2206 (35db4e3f7))*

## Papers

1. **[Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)** *(2020)* `measured` Standard test sets come from the same distribution as the training data, so a dataset with systematic gaps or annotation artifacts lets models score well with shallow heuristics.
2. **[TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://md.research.elamur.ai/free/en/papers/ning-2020-torque-reading-comprehension-dataset-temporal/)** *(2020)* `1 idea kept` TORQUE asks temporal relations as reading-comprehension questions instead of labelling event pairs with Allen-style relations.
3. **[DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)** *(2019)* DROP has 96,567 questions on about 7,000 Wikipedia passages rich in numbers (American-football game summaries, history), written adversarially against a reading model so that answering needs discrete operations over several places in the text.
4. **[Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)** *(2019)* Quoref has over 24K span-answer questions on 4.7K Wikipedia paragraphs (about 40% film plot summaries, which mention many characters), written by crowd workers so that answering needs linking two mentions of one entity.

## Co-authors (32)

- [Pradeep Dasigi](https://md.research.elamur.ai/free/en/authors/pradeep-dasigi/) ×3
- [Dheeru Dua](https://md.research.elamur.ai/free/en/authors/dheeru-dua/) ×2
- [Nelson F. Liu](https://md.research.elamur.ai/free/en/authors/nelson-f-liu/) ×2
- [Noah A. Smith](https://md.research.elamur.ai/free/en/authors/noah-a-smith/) ×2
- [Qiang Ning](https://md.research.elamur.ai/free/en/authors/qiang-ning/) ×2
- [Sameer Singh](https://md.research.elamur.ai/free/en/authors/sameer-singh/) ×2
- [Ally Zhang](https://md.research.elamur.ai/free/en/authors/ally-zhang/)
- [Ana Marasovic](https://md.research.elamur.ai/free/en/authors/ana-marasovic/)
- Ananth Gottumukkala
- Ben Bogin
- Ben Zhou
- [Dan Roth](https://md.research.elamur.ai/free/en/authors/dan-roth/)
- [Daniel Khashabi](https://md.research.elamur.ai/free/en/authors/daniel-khashabi/)
- Eric Wallace
- Gabriel Ilharco
- [Gabriel Stanovsky](https://md.research.elamur.ai/free/en/authors/gabriel-stanovsky/)
- [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/)
- Hao Wu
- [Jiangming Liu](https://md.research.elamur.ai/free/en/authors/jiangming-liu/)
- Jonathan Berant
- Kevin Lin
- [Nanyun Peng](https://md.research.elamur.ai/free/en/authors/nanyun-peng/)
- Nitish Gupta
- Phoebe Mulcaire
- [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/)
- Rujun Han
- Sanjay Subramanian
- Sihao Chen
- Victoria Basmov
- Yanai Elazar
- Yizhong Wang
- [Yoav Artzi](https://md.research.elamur.ai/free/en/authors/yoav-artzi/)

## More papers in the library (12)

Pages of the Research library: our summary, the ideas, references and who cites each.

- [Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks](https://md.research.elamur.ai/free/en/papers/asai-2022-evidentiality-guided-generation-knowledge-intensive/) — Akari Asai, Matt Gardner, Hannaneh Hajishirzi · 2022
- [Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) — Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al. · 2022
- [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://md.research.elamur.ai/free/en/papers/dasigi-2021-dataset-information-seeking-questions-answers/) — Pradeep Dasigi, Kyle Lo, Iz Beltagy, et al. · 2021
- [Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wu-2021-understanding-mention-detector-linker-interaction/) — Zhaofeng Wu, Matt Gardner · 2021
- [Break It Down: A Question Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/wolfson-2020-break-it-down-question-understanding/) — Tomer Wolfson, Mor Geva, Ankit Gupta, et al. · 2020
- [Compositional Questions Do Not Necessitate Multi-hop Reasoning](https://md.research.elamur.ai/free/en/papers/min-2019-compositional-questions-do-not-necessitate/) — Sewon Min, Eric Wallace, Sameer Singh, et al. · 2019
- [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://md.research.elamur.ai/free/en/papers/wallace-2019-do-nlp-models-know-numbers/) — Eric Wallace, Yizhong Wang, Sujian Li, et al. · 2019
- [Linguistic Knowledge and Transferability of Contextual Representations](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/) — Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al. · 2019
- [Reasoning Over Paragraph Effects in Situations](https://md.research.elamur.ai/free/en/papers/lin-2019-reasoning-over-paragraph-effects-situations/) — Kevin Lin, Oyvind Tafjord, Peter Clark, et al. · 2019
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/) — Matt Gardner, Joel Grus, Mark Neumann, et al. · 2018
- [Crowdsourcing Multiple Choice Science Questions](https://md.research.elamur.ai/free/en/papers/welbl-2017-crowdsourcing-multiple-choice-science-questions/) — Johannes Welbl, Nelson F. Liu, Matt Gardner · 2017
- [Simple and Effective Multi-Paragraph Reading Comprehension](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/) — Christopher Clark, Matt Gardner · 2017