activity
20182023
most citedEvaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks

11 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2023

Is Cross-modal Information Retrieval Possible without Training?

Hyunjin Choi, Hyunjae Lee, Seongho Joe +1

Encoded representations from a pretrained deep learning model (e.g., BERT text embeddings, penultimate CNN layer activations of an image) convey a rich set of features beneficial f…

cs.LG2022★ 2 cited

Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

Minkyu Kim, Hyun-Soo Choi, Jinho Kim

Neural Additive Models (NAMs) have recently demonstrated promising predictive performance while maintaining interpretability. However, their capacity is limited to capturing only f…

cs.CL2022★ 4 cited

Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization

Hyunjae Lee, Jaewoong Yun, Hyunjin Choi +2

Contextualized word embeddings can lead to state-of-the-art performances in natural language understanding. Recently, a pre-trained deep contextualized text encoder such as BERT ha…

cs.CL2021★ 1 cited

Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks

Hyunjin Choi, Judong Kim, Seongho Joe +2

In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training. A source…

cs.CL2021★ 11 cited

Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks

Hyunjin Choi, Judong Kim, Seongho Joe +1

Contextualized representations from a pre-trained language model are central to achieve a high performance on downstream NLP task. The pre-trained BERT and A Lite BERT (ALBERT) mod…

cs.LG2020

DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

Yonghyun Jeong, Hyunjin Choi, Byoungjip Kim +1

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed sta…

cs.AI2018★ 2 cited

Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders

Hyungu Kahng, Yonghyun Jeong, Yoon Sang Cho +12

StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level stra…