Deep Learning for Natural Language Processing (Recruiting)
Description
Deep learning methods have been applied for solving a wide range of natural language processing (NLP) tasks such as dialog, summarization, and question answering.
You can suggest any NLP tasks you want to attack. The
recent publications of our lab would help to choose the task.
Candidate Qualifications
- Strong attitude for the investigation
- Basic python skills (required)
- Understanding of recent algorithms in NLP literature. e.g., BERT.
- Experience in implementing algorithms using PyTorch or Tensorflow
Expected Internship Period
- Minimum three months - excluding period for the preliminary study (Pytorch, base ML, NLP basics)
Contact
Neuro-symbolic Deep Learning for Logical Inference (Completed)
Description
Abilities to do logical inference, i.e. rule-based generalization, distinguish human intelligence from contemporary artificial intelligence (AI) implemented by deep learning models.
To bridge this gap, studies about neuro-symbolic deep learning approach have been conducted.
In this internship, we aim to digest existing works about the neuro-symbolic approach by reading papers and analyzing their implementation codes.
To go further, we implement our own ideas and tackle challenging systematic generalization problems.
Candidate Qualifications
- Strong and persistent attitude for the research (required)
- Basic python skills (required)
- PyTorch experience (optional)
Expected Internship Period
Contact
End-to-end text-to-speech (Completed)
Description
Investigating end-to-end learning approaches to building a text-to-speech system that converts natural language text into human speech. The internship includes research, implementation, and experiment for target tasks above.
Candidate Qualifications
- Strong attitude for the investigation
- Basic python skills (required)
- Understanding of recent algorithm in deep learning literature (i.e. Tacotron from google)
- Experience in implementing an algorithm using PyTorch or Tensorflow
Expected Internship Period
- Minimum four months - excluding period for the preliminary study (Pytorch, base ML algorithms)
Contact
Yoonhyung Lee (cpi1234@snu.ac.kr) (Completed)
Adversarial text generation (Completed)
Description
Adversarial Text generation aims at generating text
with Generative adversarial networks (GANs) which can alleviate
those drawbacks of MLE-based models such as exposure bias.
You will learn the procedure of deep learning research which includes design and implementation of deep learning models. In addition, you will be involved in the research in progress.
Candidate Qualifications
- Strong attitude for the investigation
- Basic python skills (required)
- Familiar with fundamental deep learning models (FNNs, CNNs, RNNs and etc)
- Experience of implementing algorithms using deep learning open-source software library (Optional)
Expected Internship Period
- Minimum three months - excluding period for the preliminary study (deep learning basics, library)
Contact
Yanghoon Kim (ad26kr@snu.ac.kr) (Completed)
