---
title: "Mo Yu — papers and cards"
description: "Mo Yu: 2 paper cards in the Research library (2022), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/mo-yu/"
---
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# Mo Yu

2 paper cards in the Research library · 2022

Mo Yu wrote 2 of the papers Mova reads, in 2022; often with [Bingsheng Yao](https://md.research.elamur.ai/free/en/authors/bingsheng-yao/), [Dakuo Wang](https://md.research.elamur.ai/free/en/authors/dakuo-wang/), [Toby Jia-Jun Li](https://md.research.elamur.ai/free/en/authors/toby-jia-jun-li/).

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

## Papers

1. **[Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension](https://md.research.elamur.ai/free/en/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. **[It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://md.research.elamur.ai/free/en/papers/yao-2022-it-is-ai-s-turn/)** *(2022)* The paper generates question–answer pairs for children's storybooks the way a teacher would ask them, using FairytaleQA (278 classic fairy tales, 10,580 QA pairs written by education experts and labelled with seven narrative elements from Paris & Paris: character, setting, feeling, action, causal relationship…

## Co-authors (17)

- [Bingsheng Yao](https://md.research.elamur.ai/free/en/authors/bingsheng-yao/) ×2
- [Dakuo Wang](https://md.research.elamur.ai/free/en/authors/dakuo-wang/) ×2
- [Toby Jia-Jun Li](https://md.research.elamur.ai/free/en/authors/toby-jia-jun-li/) ×2
- [Tongshuang Wu](https://md.research.elamur.ai/free/en/authors/tongshuang-wu/) ×2
- [Ying Xu](https://md.research.elamur.ai/free/en/authors/ying-xu/) ×2
- [Zheng Zhang](https://md.research.elamur.ai/free/en/authors/zheng-zhang/) ×2
- Branda Sun
- Daniel Ritchie
- [Diyi Yang](https://md.research.elamur.ai/free/en/authors/diyi-yang/)
- Mark Warschauer
- [Nanyun Peng](https://md.research.elamur.ai/free/en/authors/nanyun-peng/)
- Nora Bradford
- Tran Bao Hoang
- Xiaojuan Ma
- Yisi Sang
- [Yufang Hou](https://md.research.elamur.ai/free/en/authors/yufang-hou/)
- [Zhou Yu](https://md.research.elamur.ai/free/en/authors/zhou-yu/)

## More papers in the library (3)

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

- [On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://md.research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/) — Nouha Dziri, Sivan Milton, Mo Yu, et al. · 2022
- [A Structured Self-attentive Sentence Embedding](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/) — Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al. · 2017
- [Improving Lexical Embeddings with Semantic Knowledge](https://md.research.elamur.ai/free/en/papers/yu-2014-improving-lexical-embeddings-semantic-knowledge/) — Mo Yu, Mark Dredze · 2014