177 citations · 189 across the 12 of their papers we have counts for
13 papers
Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models
Kyudan Jung, Jihwan Kim, Soyoon Kim +3
As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer i…
SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
Kyudan Jung, Jihwan Kim, Minwoo Lee +4
Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the e…
PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction
Chung Park, Junui Hong, Cheonbok Park +3
Understanding the movement patterns of objects (e.g., humans and vehicles) in a city is essential for many applications, including city planning and management. This paper proposes…
Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints
Yujin Baek, Koanho Lee, Dayeon Ki +3
Lexically-constrained NMT (LNMT) aims to incorporate user-provided terminology into translations. Despite its practical advantages, existing work has not evaluated LNMT models unde…
Specializing Multi-domain NMT via Penalizing Low Mutual Information
Jiyoung Lee, Hantae Kim, Hyunchang Cho +2
Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. It is appealing because of its efficacy in handling multiple domains within one model. An…
Residual Correction in Real-Time Traffic Forecasting
Daejin Kim, Youngin Cho, Dongmin Kim +2
Predicting traffic conditions is tremendously challenging since every road is highly dependent on each other, both spatially and temporally. Recently, to capture this spatial and t…
PePe: Personalized Post-editing Model utilizing User-generated Post-edits
Jihyeon Lee, Taehee Kim, Yunwon Tae +2
Incorporating personal preference is crucial in advanced machine translation tasks. Despite the recent advancement of machine translation, it remains a demanding task to properly r…
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
Seungmin Jin, Hyunwook Lee, Cheonbok Park +4
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the t…
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation
Cheonbok Park, Hantae Kim, Ioan Calapodescu +2
Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies on a pre-trained general NMT model which is adapted to the new domain on a sample of in-domain paralle…
An Empirical Experiment on Deep Learning Models for Predicting Traffic Data
Hyunwook Lee, Cheonbok Park, Seungmin Jin +3
To tackle ever-increasing city traffic congestion problems, researchers have proposed deep learning models to aid decision-makers in the traffic control domain. Although the propos…
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
Cheonbok Park, Yunwon Tae, Taehee Kim +4
Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However,…
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
Cheonbok Park, Chunggi Lee, Hyojin Bahng +5
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamic…
SANVis: Visual Analytics for Understanding Self-Attention Networks
Cheonbok Park, Inyoup Na, Yongjang Jo +7
Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many oth…