/NMT-Korean-To-English

Deep Learning Algorithm을 활용한 한영 번역기 개발

Primary LanguagePython

NMT-Koean-To-English

작성중...

  • 한영 기계 번역(Korean-English Machine Translation) 모델 개발 스터디
  • PyTorch, koNLPY, NLPY, Gensim package 활용

Requirements

  • Python 3.6 (may work with other versions, but I used 3.6)
  • PyTorch 1.1.0
  • Gensim 3.8.0
  • konlpy 0.5.1
  • nltk 3.4.4

Datasets

git clone https://github.com/dlcjfgmlnasa/NMT-Koean-To-English.git --recursive

pip install -r requirement.txt

목차

  1. Sequence to Sequence (Seq2Seq)
  2. Sequence to Sequence with Attention (Seq2Seq with Attention)
  3. Convolution Sequence to Sequence
  4. ByteNet
  5. SliceNet
  6. Transformer

1. Seq2Seq

  • Parameter List
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--rnn_sequence_size', default=30, type=int)
parser.add_argument('--min_count', default=3, type=int)
parser.add_argument('--max_count', default=10000, type=int)
parser.add_argument('--embedding_size', default=200, type=int)
parser.add_argument('--rnn_dim', default=200, type=int)
parser.add_argument('--rnn_layer', default=3, type=int)
parser.add_argument('--batch_size', default=128, type=int)
  • loss

Seq2Seq Loss function

  • translation

2. Seq2Seq with Attention

  • Parameter List
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--rnn_sequence_size', default=30, type=int)
parser.add_argument('--min_count', default=3, type=int)
parser.add_argument('--max_count', default=100000, type=int)
parser.add_argument('--embedding_size', default=200, type=int)
parser.add_argument('--rnn_dim', default=123, type=int)
parser.add_argument('--rnn_layer', default=3, type=int)
parser.add_argument('--rnn_dropout_rate', default=0.5, type=float)
parser.add_argument('--use_residual', default=True, type=bool)
parser.add_argument('--attention_method', default='general', choices=['dot', 'general', 'concat'], type=str)
parser.add_argument('--batch_size', default=128, type=int)

3. Convolution Seq2Seq

  • 구현 완료 (2019.08.05)

4. ByteNet

5. SliceNet

6. Transformer

Reference