The papers were implemented in using korean corpus

• Using the Naver sentiment movie corpus v1.0
• Hyper-parameter was arbitrarily selected. (epoch: 5, mini_batch: 128)

• Convolutional Neural Networks for Sentence Classification (as SenCNN)
  • https://arxiv.org/abs/1408.5882
• Character-level Convolutional Networks for Text Classification (as CharCNN)
  • https://arxiv.org/abs/1509.01626
• Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers (as ConvRec)
  • https://arxiv.org/abs/1602.00367
• Very Deep Convolutional Networks for Text Classification (as VDCNN)
  • https://arxiv.org/abs/1606.01781
• A Structured Self-attentive Sentence Embedding (as SAN)
  • https://arxiv.org/abs/1703.03130

• Creating dataset from https://github.com/songys/Question_pair
• Hyper-parameter was arbitrarily selected. (epoch: 5, mini_batch: 64)

• A Structured Self-attentive Sentence Embedding (as SAN)
  • https://arxiv.org/abs/1703.03130

• Character-Aware Neural Language Models
  • https://arxiv.org/abs/1508.06615

• Using the Naver nlp-challange corpus for NER
• Hyper-parameter was arbitrarily selected.

• Bidirectional LSTM-CRF Models for Sequence Tagging
  • https://arxiv.org/abs/1508.01991
• End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
  • https://arxiv.org/abs/1603.01354
• Neural Architectures for Named Entity Recognition
  • https://arxiv.org/abs/1603.01360

• Effective Approaches to Attention-based Neural Machine Translation
  • https://arxiv.org/abs/1508.04025
• Attention Is All You Need
  • https://arxiv.org/abs/1706.03762

• Bi-directional attention flow for machine comprehension
  • https://arxiv.org/abs/1611.01603

• Deep contextualized word representations
  • https://arxiv.org/abs/1802.05365
• Improving Language Understanding by Generative Pre-Training
  • https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  • https://arxiv.org/abs/1810.04805
• Language Models are Unsupervised Multitask Learners
  • https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf