Standard encoder-decoder NMT (following Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Y. Wu et el)

• python 3.6
• torch 1.2
• tensorboard 1.14+
• psutil
• dill
• CUDA 9

• Source / target files: one sentence per line
• Source / target vocab files: one vocab per line, the top 5 fixed to be <pad> <unk> <s> </s> <spc> as defined in utils/config.py

To train the model - check Examples/train.sh

• train_path_src - path to source file for training
• train_path_tgt - path to target file for training
• dev_path_src - path to source file for validation (default set to None)
• dev_path_tgt - path to target file for validation (default set to None)
• path_vocab_src - path to source vocab list
• path_vocab_tgt - path to target vocab list
• load_embedding_src - load pretrained src embedding if provided
• load_embedding_tgt - load pretrained target embedding if provided
• use_type - word or tokenise into char
• save - dir to save the trained model
• random_seed - set random seed
• share_embedder - share embedding matrix across source and target
• embedding_size_enc - source embedding size
• embedding_size_dec - target embedding size
• hidden_size_enc - encoder hidden size
• num_bilstm_enc - number of encoder BiLSTM layers
• num_unilstm_enc - number of encoder UniLSTM layers (default 0)
• hidden_size_dec - decoder hidden size
• num_unilstm_dec - number of decoder UniLSTM layers
• att_mode - attention mode bahdanau | bilinear | hybrid
• hidden_size_att - only used if att_mode is set to hybrid
• residual - residual connection across LSTM layers
• hidden_size_shared - transformed attention output hidden size
• max_seq_len - maximum sequence length, longer sentences filtered out in training
• batch_size - batch size
• batch_first - set to True
• seqrev - train seq2seq in reverse order
• eval_with_mask - compute loss on non <pad> tokens (default True)
• scheduled_sampling - scheduled sampling
• teacher_forcing_ratio - probability to run in teacher forcing mode, set to 1.0 for teacher forcing to be used throughout
• dropout - dropout rate
• embedding_dropout - embedding dropout rate
• num_epochs - number of epochs
• use_gpu - set to True if GPU device is available
• learning_rate - learning rate
• max_grad_norm - gradient clipping
• checkpoint_every - number of batches trained for 1 checkpoint saved (if dev_path* not given, save after every epoch)
• print_every - number of batches trained for train losses printed
• max_count_no_improve - used when dev_path* is given, number of batches trained (with no improvement in accuracy on dev set) before roll back
• max_count_num_rollback - reduce learning rate if rolling back for multiple times
• keep_num - number of checkpoint kept in model dir (used if dev_path* is given)
• normalise_loss - normalise loss on per token basis
• minibatch_split - if OOM, split batch into minibatch (note gradient descent still is done per batch, not minibatch)

To test the model - check Examples/translate.sh

• test_path_src - path to source text
• seqrev - translate in reverse order or not
• path_vocab_src - be consistent with training
• path_vocab_tgt - be consistent with training
• use_type - be consistent with training
• load - path to model checkpoint
• test_path_out - path to save the translated text
• max_seq_len - maximum translation sequence length (set to be at least larger than the maximum source sentence length)
• batch_size - batch size in translation, restricted by memory
• use_gpu - set to True if GPU device is available
• beam_width - beam search decoding
• eval_mode - default 1 (other modes for debugging)