NLP paper implementation relevant to classification with PyTorch

The papers were implemented in using korean corpus

Prelimnary & Usage

• preliminary

pyenv virualenv 3.7.7 nlp
pyenv activate nlp
pip install -r requirements.txt

• Usage

python build_dataset.py
python build_vocab.py
python train.py # default training parameter
python evaluate.py # defatul evaluation parameter

Single sentence classification (sentiment classification task)

• Using the Naver sentiment movie corpus v1.0 (a.k.a. nsmc)
• Configuration
  • conf/model/{type}.json (e.g. type = ["sencnn", "charcnn",...])
  • conf/dataset/nsmc.json
• Structure

# example: Convolutional_Neural_Networks_for_Sentence_Classification
├── build_dataset.py
├── build_vocab.py
├── conf
│   ├── dataset
│   │   └── nsmc.json
│   └── model
│       └── sencnn.json
├── evaluate.py
├── experiments
│   └── sencnn
│       └── epochs_5_batch_size_256_learning_rate_0.001
├── model
│   ├── data.py
│   ├── __init__.py
│   ├── metric.py
│   ├── net.py
│   ├── ops.py
│   ├── split.py
│   └── utils.py
├── nsmc
│   ├── ratings_test.txt
│   ├── ratings_train.txt
│   ├── test.txt
│   ├── train.txt
│   ├── validation.txt
│   └── vocab.pkl
├── train.py
└── utils.py

• [x] Convolutional Neural Networks for Sentence Classification (as SenCNN)
  • https://arxiv.org/abs/1408.5882
• [x] Character-level Convolutional Networks for Text Classification (as CharCNN)
  • https://arxiv.org/abs/1509.01626
• [x] Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers (as ConvRec)
  • https://arxiv.org/abs/1602.00367
• [x] Very Deep Convolutional Networks for Text Classification (as VDCNN)
  • https://arxiv.org/abs/1606.01781
• [x] A Structured Self-attentive Sentence Embedding (as SAN)
  • https://arxiv.org/abs/1703.03130
• [x] BERT_single_sentence_classification (as ETRIBERT, SKTBERT)
  • https://arxiv.org/abs/1810.04805

Pairwise-text-classification (paraphrase detection task)

• Creating dataset from https://github.com/songys/Question_pair
• Configuration
  • conf/model/{type}.json (e.g. type = ["siam", "san",...])
  • conf/dataset/qpair.json
• Structure

# example: Siamese_recurrent_architectures_for_learning_sentence_similarity
├── build_dataset.py
├── build_vocab.py
├── conf
│   ├── dataset
│   │   └── qpair.json
│   └── model
│       └── siam.json
├── evaluate.py
├── experiments
│   └── siam
│       └── epochs_5_batch_size_64_learning_rate_0.001
├── model
│   ├── data.py
│   ├── __init__.py
│   ├── metric.py
│   ├── net.py
│   ├── ops.py
│   ├── split.py
│   └── utils.py
├── qpair
│   ├── kor_pair_test.csv
│   ├── kor_pair_train.csv
│   ├── test.txt
│   ├── train.txt
│   ├── validation.txt
│   └── vocab.pkl
├── train.py
└── utils.py

• [x] A Structured Self-attentive Sentence Embedding (as SAN)
  • https://arxiv.org/abs/1703.03130
• [x] Siamese recurrent architectures for learning sentence similarity (as Siam)
  • https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12195
• [x] Stochastic Answer Networks for Natural Language Inference (as Stochastic)
  • https://arxiv.org/abs/1804.07888
• [x] BERT_pairwise_text_classification (as ETRIBERT, SKTBERT)
  • https://arxiv.org/abs/1810.04805