Implementation of the paper "Document-Level Neural Machine Translation with Hierarchical Attention Networks". It is based on OpenNMT (v.2.1) https://github.com/OpenNMT/OpenNMT-py
This is a restricted version. It DOES NOT work for shards, and multimodal translation.
The data, similary for any NMT baseline, consists of a source file and a target file which are aligned at sentence-level. However, the sentences should be in order for each document (i.e. not shuffled). Additionally, the model requires a file (doc_file) indicating the beginning of each document in the source file. Each line of the doc_file indicates the number of lines at the source file where a new document starts.
Example:
0
10
25
There are 3 documents. The first one from line 0 to line 9, the second from line 10 to 24, the third from line 25 to the end.
Command:
python preprocess.py -train_src [source_file] -train_tgt [target_file] -train_doc [doc_file]
-valid_src [source_dev_file] -valid_tgt [target_dev_file] -valid_doc [doc_dev_file] -save_data [out_file]
The folder preprocess_TED_zh-en contains the files to preprocess the TED Talks zh-en dataset from https://wit3.fbk.eu/mt.php?release=2015-01.
Training the sentence-level NMT baseline:
python train.py -data [data_set] -save_model [sentence_level_model] -encoder_type transformer -decoder_type transformer -enc_layers 6 -dec_layers 6 -label_smoothing 0.1 -src_word_vec_size 512 -tgt_word_vec_size 512 -rnn_size 512 -position_encoding -dropout 0.1 -batch_size 4096 -start_decay_at 20 -report_every 500 -epochs 20 -gpuid 0 -max_generator_batches 16 -batch_type tokens -normalization tokens -accum_count 4 -optim adam -adam_beta2 0.998 -decay_method noam -warmup_steps 8000 -learning_rate 2 -max_grad_norm 0 -param_init 0 -param_init_glorot
-train_part sentences
Training HAN-encoder using the sentence-level NMT model:
python train.py -data [data_set] -save_model [HAN_enc_model] -encoder_type transformer -decoder_type transformer -enc_layers 6 -dec_layers 6 -label_smoothing 0.1 -src_word_vec_size 512 -tgt_word_vec_size 512 -rnn_size 512 -position_encoding -dropout 0.1 -batch_size 1024 -start_decay_at 2 -report_every 500 -epochs 1 -gpuid 0 -max_generator_batches 32 -batch_type tokens -normalization tokens -accum_count 4 -optim adam -adam_beta2 0.998 -decay_method noam -warmup_steps 8000 -learning_rate 2 -max_grad_norm 0 -param_init 0 -param_init_glorot
-train_part all -context_type HAN_enc -context_size 3 -train_from [sentence_level_model]
Training HAN-decoder using the sentence-level NMT model:
python train.py -data [data_set] -save_model [HAN_dec_model] -encoder_type transformer -decoder_type transformer -enc_layers 6 -dec_layers 6 -label_smoothing 0.1 -src_word_vec_size 512 -tgt_word_vec_size 512 -rnn_size 512 -position_encoding -dropout 0.1 -batch_size 1024 -start_decay_at 2 -report_every 500 -epochs 1 -gpuid 0 -max_generator_batches 32 -batch_type tokens -normalization tokens -accum_count 4 -optim adam -adam_beta2 0.998 -decay_method noam -warmup_steps 8000 -learning_rate 2 -max_grad_norm 0 -param_init 0 -param_init_glorot
-train_part all -context_type HAN_dec -context_size 3 -train_from [sentence_level_model]
Training HAN-joint using the HAN-encoder model:
python train.py -data [data_set] -save_model [HAN_joint_model] -encoder_type transformer -decoder_type transformer -enc_layers 6 -dec_layers 6 -label_smoothing 0.1 -src_word_vec_size 512 -tgt_word_vec_size 512 -rnn_size 512 -position_encoding -dropout 0.1 -batch_size 1024 -start_decay_at 2 -report_every 500 -epochs 1 -gpuid 0 -max_generator_batches 32 -batch_type tokens -normalization tokens -accum_count 4 -optim adam -adam_beta2 0.998 -decay_method noam -warmup_steps 8000 -learning_rate 2 -max_grad_norm 0 -param_init 0 -param_init_glorot
-train_part all -context_type HAN_join -context_size 3 -train_from [HAN_enc_model]
Input options:
- train_part: [sentences, context, all]
- context_type: [HAN_enc, HAN_dec, HAN_join, HAN_dec_source, HAN_dec_context]
- context_size: number of previous sentences
NOTE: The transformer model is sensitive to variation on hyperparameters. The HAN is also sensitive to the batch size.
The translation is done sentence by sentence despite not being necesary for HAN_enc or baseline (this could be improved).
python translate.py -model [model] -src [test_source_file] -doc [test_doc_file]
-output [out_file] -translate_part all -batch_size 1000 -gpu 0
Input options:
- translate_part: [sentences, all]
- batch_size: maximun number of sentences to keep in memory at once.
The output files of the 3 reported systems: transformer NMT, cache NMT, HAN-decoder NMT, HAN-encoder NMT, HAN-encoder-decoder NMT.
- sub_es-en: Opensubtitles
- sub_zh-en: TV subtitles
- TED_es-en: TED Talks WIT 2015
- TED_zh-en: TED Talks WIT 2014
Miculicich, L., Ram, D., Pappas, N. & Henderson, J. Document-Level Neural Machine Translation with Hierarchical Attention Networks. EMNLP 2018. https://www.aclweb.org/anthology/D18-1325/
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