Search Results for author: Gyuwan Kim

Found 15 papers, 9 papers with code

Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation

1 code implementation25 Oct 2022 Soyoung Yoon, Sungjoon Park, Gyuwan Kim, Junhee Cho, Kihyo Park, Gyutae Kim, Minjoon Seo, Alice Oh

We show that the model trained with our datasets significantly outperforms the currently used statistical Korean GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets.

Attribute Diversity +1

Bridging the Training-Inference Gap for Dense Phrase Retrieval

no code implementations25 Oct 2022 Gyuwan Kim, Jinhyuk Lee, Barlas Oguz, Wenhan Xiong, Yizhe Zhang, Yashar Mehdad, William Yang Wang

Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search.

Open-Domain Question Answering Passage Retrieval +1

Consistency Training with Virtual Adversarial Discrete Perturbation

1 code implementation NAACL 2022 Jungsoo Park, Gyuwan Kim, Jaewoo Kang

Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar.

Semi-Supervised Text Classification

Two-stage Textual Knowledge Distillation for End-to-End Spoken Language Understanding

1 code implementation25 Oct 2020 Seongbin Kim, Gyuwan Kim, Seongjin Shin, Sangmin Lee

End-to-end approaches open a new way for more accurate and efficient spoken language understanding (SLU) systems by alleviating the drawbacks of traditional pipeline systems.

Ranked #3 on Spoken Language Understanding on Fluent Speech Commands (using extra training data)

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search

1 code implementation ACL 2021 Gyuwan Kim, Kyunghyun Cho

We then conduct a multi-objective evolutionary search to find a length configuration that maximizes the accuracy and minimizes the efficiency metric under any given computational budget.

Question Answering text-classification +1

Large Product Key Memory for Pretrained Language Models

no code implementations Findings of the Association for Computational Linguistics 2020 Gyuwan Kim, Tae-Hwan Jung

Product key memory (PKM) proposed by Lample et al. (2019) enables to improve prediction accuracy by increasing model capacity efficiently with insignificant computational overhead.

Causal Language Modeling Language Modelling

AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights

4 code implementations ICLR 2021 Byeongho Heo, Sanghyuk Chun, Seong Joon Oh, Dongyoon Han, Sangdoo Yun, Gyuwan Kim, Youngjung Uh, Jung-Woo Ha

Because of the scale invariance, this modification only alters the effective step sizes without changing the effective update directions, thus enjoying the original convergence properties of GD optimizers.

Audio Classification Image Classification +3

Efficient Dialogue State Tracking by Selectively Overwriting Memory

3 code implementations ACL 2020 Sungdong Kim, Sohee Yang, Gyuwan Kim, Sang-Woo Lee

This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations.

Decoder Dialogue State Tracking +1

Subword Language Model for Query Auto-Completion

1 code implementation IJCNLP 2019 Gyuwan Kim

We present how to utilize subword language models for the fast and accurate generation of query completion candidates.

Language Modelling

Training IBM Watson using Automatically Generated Question-Answer Pairs

no code implementations12 Nov 2016 Jangho Lee, Gyuwan Kim, Jaeyoon Yoo, Changwoo Jung, Minseok Kim, Sungroh Yoon

Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy.

Answer Generation Question-Answer-Generation +1

NASCUP: Nucleic Acid Sequence Classification by Universal Probability

1 code implementation16 Nov 2015 Sunyoung Kwon, Gyuwan Kim, Byunghan Lee, Jongsik Chun, Sungroh Yoon, Young-Han Kim

Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statistical structures of nucleotide sequences by compact context-tree models and universal probability from information theory.

Genomics Information Theory Information Theory

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