profile picture

Seanie Lee

Ph.d. Student

Education

PhD in Artificial Intelligence

Korea Advanced Institute of Science and Technology
2022 - present

M.S. in Artificial Intelligence

Korea Advanced Institute of Science and Technology
2020 - 2022

B.A. in Library and Information Science

Yonsei University
2011- 2018

Languages

  • Korean (Native)
  • English (Intermediate)

Interests

  • Natural Language Processing
  • Bayesian Deep Learning
  • Mathematics
  • Statistics

Career Profile

I am a PhD student at Machine Learning and Artificial Intelligence (MLAI) lab in KAIST, working on natural language processing and machine learning. I am fortunate to be advised by Sung Ju Hwang and Juho Lee. Here is my CV.

Publications

  • Set Based Stochastic Subsampling
    • Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang
    ICML 2022
  • Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
    • Seanie Lee*, Hae Beom Lee*, Juho Lee, Sung Ju Hwang
    ICLR 2022
  • Learning to Perturb Word Embeddings for Out-of-distribution QA
    • Seanie Lee*, Minki Kang*, Juho Lee, Sung Ju Hwang
    ACL 2021
  • Self-supervised Text-to-SQL Learning with Header Alignment Training
    • Donggyu kim, Seanie Lee
    arxiv preprint
  • Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
    • Seanie Lee*, Dong Bok Lee*, Sung Ju Hwang
    ICLR 2021
  • Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
    • Dong Bok Lee, Dongchan Min, Seanie Lee, Sung Ju Hwang
    ICLR 2021
  • Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
    • Dong Bok Lee*, Seanie Lee*, WooTae Jeong, Donghwan Kim, Sung Ju Hwang
    ACL 2020
  • g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset
    • Kyubyong Park*, Seanie Lee*
    INTERSPEECH 2020
  • Domain-agnostic Question-Answering with Adversarial Training
    • Seanie Lee*, Donggyu Kim*, Jangwon Park*.
    EMNLP-IJCNLP 2019 MRQA Workshop shared task
  • Incorporating product description to sentiment topic models for improved aspect-based sentiment analysis
    • Reinald Kim Amplayo, Seanie Lee, and Min Song.
    Information Sciences 454 (2018): 200-215

    Experiences

    Research Intern

    2019.2 - 2020.1
    42Maru, Seoul
    • Research on question generation and semi-supervised learning
    • Domain adaptation for Question Answering

    Projects

    Group study of Mathematical Statistics - Group Study of mathematical statistics. The main text book is mathematical statistics by 김우철
    Benchmark dataset and package for Chinese Grapheme to Phoneme - Release a new open-source benchmark dataset for Chinese Grapheme to Phoneme and python package
    Pytorch implementation for Question Generation - Pytorch implementation for the paper Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks.
    Group study of Linear Algebra - Group Study of Linear Algebra. The main text book is Elementary Linear Algebra (Friedberg et al) and 선형대수와 군 (Linear Algebra and Group)
    Silver Medal in NLP challenge - Won silver and bronze medal in NER (Named Entity Recognition) and SRL (Semantic Role Labeling).

    Skills & Proficiency

    Natual Language Processing

    Deep Learning

    Mathematics

    Statistics