277 citations · 1.6k across the 56 of their papers we have counts for
11 papers · 1 filter
DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances
Xiaodong Gu, Kang Min Yoo, Jung-Woo Ha
Recent advances in pre-trained language models have significantly improved neural response generation. However, existing methods usually view the dialogue context as a linear seque…
Context-Aware Answer Extraction in Question Answering
Yeon Seonwoo, Ji-Hoon Kim, Jung-Woo Ha +1
Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage. However, they sometimes result in predicting the corr…
ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding
Minjeong Kim, Gyuwan Kim, Sang-Woo Lee +1
Language model pre-training has shown promising results in various downstream tasks. In this context, we introduce a cross-modal pre-trained language model, called Speech-Text BERT…
CareCall: a Call-Based Active Monitoring Dialog Agent for Managing COVID-19 Pandemic
Sang-Woo Lee, Hyunhoon Jung, SukHyun Ko +17
Tracking suspected cases of COVID-19 is crucial to suppressing the spread of COVID-19 pandemic. Active monitoring and proactive inspection are indispensable to mitigate COVID-19 sp…
Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty
Wonyoung Shin, Jung-Woo Ha, Shengzhe Li +3
Label noise is a critical factor that degrades the generalization performance of deep neural networks, thus leading to severe issues in real-world problems. Existing studies have e…
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the repre…
Graphs, Entities, and Step Mixture
Kyuyong Shin, Wonyoung Shin, Jung-Woo Ha +1
Existing approaches for graph neural networks commonly suffer from the oversmoothing issue, regardless of how neighborhoods are aggregated. Most methods also focus on transductive…
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +1
The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognit…
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Byeongho Heo, Sanghyuk Chun, Seong Joon Oh +5
Normalization techniques are a boon for modern deep learning. They let weights converge more quickly with often better generalization performances. It has been argued that the norm…
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
Jung-Woo Ha, Kihyun Nam, Jingu Kang +11
Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services. Despite the advancement of ASR, however, most pu…
Modeling Musical Onset Probabilities via Neural Distribution Learning
Jaesung Huh, Egil Martinsson, Adrian Kim +1
Musical onset detection can be formulated as a time-to-event (TTE) or time-since-event (TSE) prediction task by defining music as a sequence of onset events. Here we propose a nove…