2 citations · 5 across the 6 of their papers we have counts for
6 papers
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Giwon Hong, Jeonghwan Kim, Junmo Kang +2
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more chal…
Maximizing Efficiency of Language Model Pre-training for Learning Representation
Junmo Kang, Suwon Shin, Jeonghwan Kim +2
Pre-trained language models in the past years have shown exponential growth in model parameters and compute time. ELECTRA is a novel approach for improving the compute efficiency o…
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval
Kyoung-Rok Jang, Junmo Kang, Giwon Hong +4
The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models' dense representatio…
Leveraging Order-Free Tag Relations for Context-Aware Recommendation
Junmo Kang, Jeonghwan Kim, Suwon Shin +1
Tag recommendation relies on either a ranking function for top- tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicti…
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
Junmo Kang, Haritz Puerto San Roman, Sung-Hyon Myaeng
Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generatin…
Visualization of Retrieved Documents using a Presentation Server
Sa-Kwang Song, Sung Hyon Myaeng
In any search-based digital library (DL) systems dealing with a non-trivial number of documents, users are often required to go through a long list of short document descriptions i…