Repo

This repo contains the code used within the paper Zero-Shot Crosslingual Sentence Simplification

For the test data and GeoLino dataset see https://github.com/Jmallins/ZEST-data

Instructions for use

There are three steps when using ZEST: Preprocessing, Training, and Predicition.

Preprocessing

First, create the vocab file (from English and German), all files should already be split into sentence pieces and ideally they should have the same tokenization (mosses tokenziation/detokenization could be used).

python preprocess.py -train_src X --train_tgt Y --share_vocab True --save_data some_dir/the_vocab

Second, create the training file per task/domain/source_language/target_language. All files should already be split into sentence pieces and ideally they should have the same tokenization (mosses tokenziation/detokenization could be used).

python preprocess.py -train_src data/src-train.txt -train_tgt data/tgt-train.txt  -save_data data/demo --src_vocab some_dir/the_vocab --tgt_vocab some_dir/the_vocab 

Third, create a json file that lists all of the training files and the source language-task-domain-target language.

{
"SOURCE_language-TASK-DOMAIN-TARGET_LANGUAGE":"file_location.train",
  "EN-TRANS-COMP-DE": "file_location.train",
  "DE-TRANS-COMP-EN": "file_location.train",
  "EN-TRANS-COMP-EN": "file_location.train",
  "EN-TRANS-SIMP-EN": "file_location.train",
  "DE-TRANS-COMP-DE": "file_location.train",
  "DE-TRANS-SIMP-DE": "file_location.train",
  "EN-LM-COMP-EN": "file_location.train",
  "EN-LM-SIMP-EN": "file_location.train",
  "DE-LM-COMP-DE": "file_location.train",
  "DE-LM-SIMP-DE": "file_location.train",
  "EN-SIMPLY-SIMPLY-SIMP-EN":"file_location.train",
  "vocab":"file_location"
  "valid-DE-SIMPLY-SIMPLY-SIMPLY-SIMP-DE":"file_location.train"
}

Being Source language, task+, domain, output language.

Training

To train a model

python  train.py -data  some_dir\something.json  -layers 6 -rnn_size 512 -word_vec_size 512 -transformer_ff 8 -heads 12         -encoder_type transformer -decoder_type transformer -position_encoding         -train_steps 250000  -max_generator_batches 2 -dropout 0.1         -batch_size 4096 -batch_type tokens -normalization tokens  -accum_count 1         -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         -label_smoothing 0.1 -valid_steps 1000 -save_checkpoint_steps 1000 --share_embeddings

Where some_dir/data.json is the json file created previously.

Predicition

python translate.py --length_penalty avg --beam_size 30  --replace_unk --dynamic_dict --share_vocab  --gpu 0 --model model_location.pt  -v --threshold 0 --ctags SIMPLY-SIMPLY-SIMPY-SIMP-DE  --batch_size 20  --max_length 128 --src input_file --output output_location