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Meng Chang Chen

Meng Chang Chen has 2 papers in our library, in 2018, most of them on Question answering, Evaluation & benchmarks, English; often with Yeali S. Sun, Yi-Ting Huang.

2 papers in our library · 2018

OpenAlex · ORCID · h-index 27 · 184 works · 3,072 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11301

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

🔬Academia SinicaTaipei · Taiwan1994–2026 · 139 paperscurrent🔬Institute of Information Science, Academia SinicaTaipei · Taiwan1994–2025 · 123 papers🔬Research Center for Information Technology Innovation, Academia SinicaTaipei · Taiwan2011–2025 · 13 papers🎓National Taiwan UniversityTaipei · Taiwan2005–2020 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksPsychology · 2 worksArtificial Intelligence · 2 worksDevelopmental and Educational Psychology · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.English · 1 workEvaluation & benchmarks · 1 workQuestion answering · 1 work

 Works by year

2010s

20182 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes (2018 · 6 citations)
    Automatic question generation can turn any English text into practice items, but earlier generators produce many questions with no testing goal and no fit to the individual learner.
  2. Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers (2018 · 3 citations)
    Most readability formulas are tuned for native speakers, but people learning English as a second language acquire vocabulary and grammar gradually and in the order their courses teach them.

 Co-authors here

2 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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