[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/matt-gardner/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Matt Gardner # Matt Gardner 4 paper cards in Mova's library · 2019–2020 Matt Gardner wrote 4 of the papers Mova reads, in 2019–2020; often with [Pradeep Dasigi](https://md.mova.elamur.ai/free/en/docs/papers/authors/pradeep-dasigi/), [Dheeru Dua](https://md.mova.elamur.ai/free/en/docs/papers/authors/dheeru-dua/), [Nelson F. Liu](https://md.mova.elamur.ai/free/en/docs/papers/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) · [21027 citations](https://openalex.org/authors/A5035088083) · [136 works](https://openalex.org/works?filter=author.id:A5035088083) — openalex, 2026-10-18 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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/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.mova.elamur.ai/free/en/docs/papers/authors/pradeep-dasigi/) ×3 - [Dheeru Dua](https://md.mova.elamur.ai/free/en/docs/papers/authors/dheeru-dua/) ×2 - [Nelson F. Liu](https://md.mova.elamur.ai/free/en/docs/papers/authors/nelson-f-liu/) ×2 - [Noah A. Smith](https://md.mova.elamur.ai/free/en/docs/papers/authors/noah-a-smith/) ×2 - [Qiang Ning](https://md.mova.elamur.ai/free/en/docs/papers/authors/qiang-ning/) ×2 - [Sameer Singh](https://md.mova.elamur.ai/free/en/docs/papers/authors/sameer-singh/) ×2 - [Ally Zhang](https://md.mova.elamur.ai/free/en/docs/papers/authors/ally-zhang/) - [Ana Marasovic](https://md.mova.elamur.ai/free/en/docs/papers/authors/ana-marasovic/) - Ananth Gottumukkala - Ben Bogin - Ben Zhou - [Dan Roth](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-roth/) - [Daniel Khashabi](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-khashabi/) - Eric Wallace - Gabriel Ilharco - [Gabriel Stanovsky](https://md.mova.elamur.ai/free/en/docs/papers/authors/gabriel-stanovsky/) - [Hannaneh Hajishirzi](https://md.mova.elamur.ai/free/en/docs/papers/authors/hannaneh-hajishirzi/) - Hao Wu - [Jiangming Liu](https://md.mova.elamur.ai/free/en/docs/papers/authors/jiangming-liu/) - Jonathan Berant - Kevin Lin - [Nanyun Peng](https://md.mova.elamur.ai/free/en/docs/papers/authors/nanyun-peng/) - Nitish Gupta - Phoebe Mulcaire - [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) - Rujun Han - Sanjay Subramanian - Sihao Chen - Victoria Basmov - Yanai Elazar - Yizhong Wang - [Yoav Artzi](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-artzi/) - [← previous Mathieu Morey](https://md.mova.elamur.ai/free/en/docs/papers/authors/mathieu-morey/) - [next → Matt Post](https://md.mova.elamur.ai/free/en/docs/papers/authors/matt-post/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)