Search Results for author: Hyunsouk Cho

Found 6 papers, 3 papers with code

Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio Representation

1 code implementation10 Jul 2022 Jeong Choi, Seongwon Jang, Hyunsouk Cho, Sehee Chung

The common research goal of self-supervised learning is to extract a general representation which an arbitrary downstream task would benefit from.

Contrastive Learning Information Retrieval +2

Self-Supervised Multimodal Opinion Summarization

no code implementations ACL 2021 Jinbae Im, Moonki Kim, Hoyeop Lee, Hyunsouk Cho, Sehee Chung

To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum.

CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation

no code implementations23 May 2021 Seongwon Jang, Hoyeop Lee, Hyunsouk Cho, Sehee Chung

To eliminate this issue, we propose a framework called CITIES, which aims to enhance the quality of the tail-item embeddings by training an embedding-inference function using multiple contextual head items so that the recommendation performance improves for not only the tail items but also for the head items.

Sequential Recommendation

SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0

no code implementations LREC 2020 Gyeongbok Lee, Seung-won Hwang, Hyunsouk Cho

Existing machine reading comprehension models are reported to be brittle for adversarially perturbed questions when optimizing only for accuracy, which led to the creation of new reading comprehension benchmarks, such as SQuAD 2. 0 which contains such type of questions.

Machine Reading Comprehension

MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

1 code implementation31 Jul 2019 Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, Sehee Chung

This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items.

Evidence Selection Meta-Learning +1

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