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# Nanyun Peng

2 paper cards in Mova's library · 2020–2022

Nanyun Peng wrote 2 of the papers Mova reads, in 2020–2022; often with [Bingsheng Yao](https://md.mova.elamur.ai/free/en/docs/papers/authors/bingsheng-yao/), Branda Sun, [Dakuo Wang](https://md.mova.elamur.ai/free/en/docs/papers/authors/dakuo-wang/).

[OpenAlex](https://openalex.org/A5030248499) · [ORCID](https://orcid.org/0000-0002-8509-6595) · [**h-index 42**](https://openalex.org/authors/A5030248499) · [8030 citations](https://openalex.org/authors/A5030248499) · [351 works](https://openalex.org/works?filter=author.id:A5030248499) — 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=Nanyun+Peng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Nanyun+Peng) · [dblp](https://dblp.org/search?q=Nanyun+Peng) · [ACL Anthology](https://aclanthology.org/search/?q=Nanyun+Peng) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Nanyun+Peng)

## 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. **[Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension](https://md.mova.elamur.ai/free/en/docs/papers/xu-2022-fantastic-questions-where-find-them/)** *(2022)* FairytaleQA is a question-answering dataset for narrative comprehension aimed at the reading level of kindergarten to eighth-grade children.
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.

## Co-authors (22)

- [Bingsheng Yao](https://md.mova.elamur.ai/free/en/docs/papers/authors/bingsheng-yao/)
- Branda Sun
- [Dakuo Wang](https://md.mova.elamur.ai/free/en/docs/papers/authors/dakuo-wang/)
- [Dan Roth](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-roth/)
- Daniel Ritchie
- [Diyi Yang](https://md.mova.elamur.ai/free/en/docs/papers/authors/diyi-yang/)
- Hao Wu
- Mark Warschauer
- [Matt Gardner](https://md.mova.elamur.ai/free/en/docs/papers/authors/matt-gardner/)
- [Mo Yu](https://md.mova.elamur.ai/free/en/docs/papers/authors/mo-yu/)
- Nora Bradford
- [Qiang Ning](https://md.mova.elamur.ai/free/en/docs/papers/authors/qiang-ning/)
- Rujun Han
- [Toby Jia-Jun Li](https://md.mova.elamur.ai/free/en/docs/papers/authors/toby-jia-jun-li/)
- [Tongshuang Wu](https://md.mova.elamur.ai/free/en/docs/papers/authors/tongshuang-wu/)
- Tran Bao Hoang
- Xiaojuan Ma
- [Ying Xu](https://md.mova.elamur.ai/free/en/docs/papers/authors/ying-xu/)
- Yisi Sang
- [Yufang Hou](https://md.mova.elamur.ai/free/en/docs/papers/authors/yufang-hou/)
- [Zheng Zhang](https://md.mova.elamur.ai/free/en/docs/papers/authors/zheng-zhang/)
- [Zhou Yu](https://md.mova.elamur.ai/free/en/docs/papers/authors/zhou-yu/)
- [← previous Najoung Kim](https://md.mova.elamur.ai/free/en/docs/papers/authors/najoung-kim/)
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