13 citations · 30 across the 11 of their papers we have counts for
11 papers
Unsupervised Dialogue Topic Segmentation in Hyperdimensional Space
Seongmin Park, Jinkyu Seo, Jihwa Lee
We present HyperSeg, a hyperdimensional computing (HDC) approach to unsupervised dialogue topic segmentation. HDC is a class of vector symbolic architectures that leverages the pro…
Toward a Better Understanding of Loss Functions for Collaborative Filtering
Seongmin Park, Mincheol Yoon, Jae-woong Lee +2
Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, lo…
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering
Jae-woong Lee, Seongmin Park, Mincheol Yoon +1
Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previo…
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving
Minjae Lee, Seongmin Park, Hyungmin Kim +6
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and laten…
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers
Minsoo Kim, Kyuhong Shim, Seongmin Park +2
Pre-trained Transformer models such as BERT have shown great success in a wide range of applications, but at the cost of substantial increases in model complexity. Quantization-awa…
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization
Seongmin Park, Beomseok Kwon, Jieun Lim +3
Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability. However, it ignores heterogene…
Leveraging Non-dialogue Summaries for Dialogue Summarization
Seongmin Park, Dongchan Shin, Jihwa Lee
To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization syst…
LIME: Weakly-Supervised Text Classification Without Seeds
Seongmin Park, Jihwa Lee
In weakly-supervised text classification, only label names act as sources of supervision. Predominant approaches to weakly-supervised text classification utilize a two-phase framew…
Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion
Seongmin Park, Jihwa Lee
We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs. Starting from well-founded assumptions about word…
Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation
Seongmin Park, Dongchan Shin, Sangyoun Paik +3
Fine-tuning pretrained language models (LMs) is a popular approach to automatic speech recognition (ASR) error detection during post-processing. While error detection systems often…
Finetuning Pretrained Transformers into Variational Autoencoders
Seongmin Park, Jihwa Lee
Text variational autoencoders (VAEs) are notorious for posterior collapse, a phenomenon where the model's decoder learns to ignore signals from the encoder. Because posterior colla…