Work-in-Progress
The purpose of this project is to explore different s2s models based on Keras Functional API
- For seq2seq model & seq2seq attn model: Translation Dataset
- get the data at: http://www.manythings.org/anki/
- For adversarial style embedding model: Sentiment Review Dataset
- get the data at: http://jmcauley.ucsd.edu/data/amazon/links.html
- For pointer network model: Integer Sequence Ordering
- get the data generation code from: https://github.com/zygmuntz/pointer-networks-experiments/blob/master/generate_data.py
- vanilla seq2seq model (Done)
- seq2seq model with attention mechanism (Done)
- seq2seq auto-encoder model with adversarial network and style embedding (Done)
- pointer network model (Work-in-Progress)
- pointer-generator model (Work-in-Progress)
- transformer model (Work-in-Progress)
- bert model (Work-in-Progress)
# For vanilla seq2seq model: Solve Translation Problem
python -m bin.seq2seq_model_train
python -m bin.seq2seq_model_test
# For seq2seq with attention mechanism model: Solve Translation Problem
python -m bin.seq2seq_attn_model_train
python -m bin.seq2seq_attn_model_test
# For seq2seq auto-encoder model with adversarial network and style embedding: Solve Style Transfer Problem
python -m bin.seq2seq_adv_style_model_train
python -m bin.seq2seq_adv_style_model_test
# For pointer network model (*Work-in-Progress*): Solve Interger Sequence Ordering Problem
python -m bin.ptr_network_model_train
python -m bin.ptr_network_model_test
- For base seq2seq: Keras Blog
- For adversarial style embedding model: Theano Implementation of Style Transfer
- Sequence to Sequence Learning with Neural Networks, 2014.
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, 2014.
- Get To The Point: Summarization with Pointer-Generator Networks, 2017.
- Style Transfer in Text: Exploration and Evaluation, 2017.
- Attention Is All You Need, 2017.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.
@misc{jonghkim,
author = {Jongho Kim},
title = {keras-seq2seq-models},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jonghkim/keras-seq2seq-model}},
}