Search Results for author: Jiho Jin

Found 8 papers, 3 papers with code

Learning from Teaching Assistants to Program with Subgoals: Exploring the Potential for AI Teaching Assistants

no code implementations19 Sep 2023 Changyoon Lee, Junho Myung, Jieun Han, Jiho Jin, Alice Oh

To compare the learners' interaction and perception of the AI and human TAs, we conducted a between-subject study with 20 novice programming learners.

KoBBQ: Korean Bias Benchmark for Question Answering

no code implementations31 Jul 2023 Jiho Jin, Jiseon Kim, Nayeon Lee, Haneul Yoo, Alice Oh, Hwaran Lee

In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework that addresses considerations for cultural adaptation of a dataset.

Question Answering

HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea

1 code implementation Findings (NAACL) 2022 Haneul Yoo, Jiho Jin, Juhee Son, JinYeong Bak, Kyunghyun Cho, Alice Oh

Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters and not understood by modern Korean or Chinese speakers.

named-entity-recognition Named Entity Recognition +3

Models and Benchmarks for Representation Learning of Partially Observed Subgraphs

1 code implementation1 Sep 2022 Dongkwan Kim, Jiho Jin, Jaimeen Ahn, Alice Oh

Subgraphs are rich substructures in graphs, and their nodes and edges can be partially observed in real-world tasks.

Representation Learning

Translating Hanja Historical Documents to Contemporary Korean and English

no code implementations20 May 2022 Juhee Son, Jiho Jin, Haneul Yoo, JinYeong Bak, Kyunghyun Cho, Alice Oh

Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English.

Machine Translation Translation

Two-Step Question Retrieval for Open-Domain QA

1 code implementation Findings (ACL) 2022 Yeon Seonwoo, Juhee Son, Jiho Jin, Sang-Woo Lee, Ji-Hoon Kim, Jung-Woo Ha, Alice Oh

These models have shown a significant increase in inference speed, but at the cost of lower QA performance compared to the retriever-reader models.

Computational Efficiency Retrieval +1

Learning Representations of Partial Subgraphs by Subgraph InfoMax

no code implementations29 Sep 2021 Dongkwan Kim, Jiho Jin, Jaimeen Ahn, Alice Oh

Subgraphs are important substructures of graphs, but learning their representations has not been studied well.

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