Search Results for author: Hwaran Lee

Found 11 papers, 4 papers with code

Why Knowledge Distillation Amplifies Gender Bias and How to Mitigate from the Perspective of DistilBERT

no code implementations NAACL (GeBNLP) 2022 Jaimeen Ahn, Hwaran Lee, JinHwa Kim, Alice Oh

Knowledge distillation is widely used to transfer the language understanding of a large model to a smaller model. However, after knowledge distillation, it was found that the smaller model is more biased by gender compared to the source large model. This paper studies what causes gender bias to increase after the knowledge distillation process. Moreover, we suggest applying a variant of the mixup on knowledge distillation, which is used to increase generalizability during the distillation process, not for augmentation. By doing so, we can significantly reduce the gender bias amplification after knowledge distillation. We also conduct an experiment on the GLUE benchmark to demonstrate that even if the mixup is applied, it does not have a significant adverse effect on the model’s performance.

Knowledge Distillation

Critic-Guided Decoding for Controlled Text Generation

no code implementations21 Dec 2022 Minbeom Kim, Hwanhee Lee, Kang Min Yoo, Joonsuk Park, Hwaran Lee, Kyomin Jung

In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding.

Language Modelling reinforcement-learning +2

Beyond Fact Verification: Comparing and Contrasting Claims on Contentious Topics

no code implementations24 May 2022 Miyoung Ko, Ingyu Seong, Hwaran Lee, Joonsuk Park, Minsuk Chang, Minjoon Seo

As the importance of identifying misinformation is increasing, many researchers focus on verifying textual claims on the web.

Fact Verification Misinformation

Plug-and-Play Adaptation for Continuously-updated QA

no code implementations Findings (ACL) 2022 Kyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, Sang-Woo Lee

To this end, we first propose a novel task--Continuously-updated QA (CuQA)--in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge.

SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System

no code implementations22 Sep 2020 Hwaran Lee, Seokhwan Jo, HyungJun Kim, SangKeun Jung, Tae-Yoon Kim

To our best knowledge, our work is the first comprehensive study of a modularized E2E multi-domain dialog system that learning from each component to the entire dialog policy for task success.

reinforcement-learning reinforcement Learning

Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech Recognition

1 code implementation6 Nov 2018 Geonmin Kim, Hwaran Lee, Bo-Kyeong Kim, Sang-Hoon Oh, Soo-Young Lee

Many speech enhancement methods try to learn the relationship between noisy and clean speech, obtained using an acoustic room simulator.

Speech Enhancement speech-recognition +1

Deep CNNs along the Time Axis with Intermap Pooling for Robustness to Spectral Variations

no code implementations10 Jun 2016 Hwaran Lee, Geonmin Kim, Ho-Gyeong Kim, Sang-Hoon Oh, Soo-Young Lee

Convolutional neural networks (CNNs) with convolutional and pooling operations along the frequency axis have been proposed to attain invariance to frequency shifts of features.

Compositional Sentence Representation from Character within Large Context Text

no code implementations2 May 2016 Geonmin Kim, Hwaran Lee, Jisu Choi, Soo-Young Lee

In the HCRN, word representations are built from characters, thus resolving the data-sparsity problem, and inter-sentence dependency is embedded into sentence representation at the level of sentence composition.

Dialogue Act Classification General Classification

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