1 code implementation • 24 Feb 2024 • Soyoung Yoon, Eunbi Choi, Jiyeon Kim, Yireun Kim, Hyeongu Yun, Seung-won Hwang
We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time.
no code implementations • 27 May 2023 • Soyoung Yoon, Chaeeun Kim, Hyunji Lee, Joel Jang, Sohee Yang, Minjoon Seo
Benchmarking the performance of information retrieval (IR) methods are mostly conducted with a fixed set of documents (static corpora); in realistic scenarios, this is rarely the case and the document to be retrieved are constantly updated and added.
1 code implementation • 25 Oct 2022 • Soyoung Yoon, Sungjoon Park, Gyuwan Kim, Junhee Cho, Kihyo Park, Gyutae Kim, Minjoon Seo, Alice Oh
We show that the model trained with our datasets significantly outperforms the currently used statistical Korean GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets.
1 code implementation • Findings (ACL) 2021 • Soyoung Yoon, Gyuwan Kim, Kyumin Park
Data augmentation with mixup has shown to be effective on various computer vision tasks.