Changyoon Lee — accepted AI-conference papers

2023

Rethinking Annotation: Can Language Learners Contribute?

Haneul Yoo, Rifki Afina Putri, Changyoon Lee, Youngin Lee, So-Yeon Ahn, Dongyeop Kang +1

ACL 2023long

Researchers have traditionally recruited native speakers to provide annotations for the widely used benchmark datasets. But there are languages for which recruiting native speakers is difficult, and it would help to get learners of those languages to annotate the data. In this paper, we investigate…

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2022

CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course

Changyoon Lee, Yeon Seonwoo, Alice Oh

NAACL 2022long

We introduce CS1QA, a dataset for code-based question answering in the programming education domain. CS1QA consists of 9,237 question-answer pairs gathered from chat logs in an introductory programming class using Python, and 17,698 unannotated chat data with code. Each question is accompanied with…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗