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20212023
most citedToward a Better Understanding of Loss Functions for Collaborative Filtering

13 citations · 30 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.CL2023★ 5 cited

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…

cs.CL2023

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…

cs.CL2022

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…

cs.CL2022★ 6 cited

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…

cs.CL2022★ 1 cited

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…

cs.CL2021★ 1 cited

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…

cs.CL2021

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…