mc-tit

Code for ACL 2023 paper: Exploring Better Text Image Translation with Multimodal Codebook

OCRMT30K Dataset

Dataset can be found here

Install fairseq

cd mc_tit
pip install -e ./

Training

Text data process

Run fiarseq-preprocess-pretrain.sh to process text data. see fairseq for more details

Stage 1

Run fairseq-pretrain.sh to pretrain model on the wmt22 dataset.

Stage 2

Run get_codebook_datasest.sh to get the multimodal codebook training data. Then, run fairseq-codebook-pretrain.sh to pretrain model on monolingual data.

Stage 3

Data construction

The method of obtaining text data is the same as in stage 1. Run fairseq-preprocess-icdar-weak.sh to handle text data. You can get image feature data in the following way:

Download the image data and organize the directory into the following form:

icdar19_lsvt_weak
├─ iwslt-weak-images
├─ image_train.txt
├─ image_valid.txt
├─ train.txt
└─ valid.txt

Run img_feat_extract/get_img_feat.py to extract image feature.

python img_feat_extract/get_img_feat.py \
    --dataset train \
    --model vit_base_patch16_224 \
    --path ../icdar19_lsvt_weak

Note that the program needs to be modified here to match your directory structure.

Then, run fairseq-ita-pretrain.sh to pretrain model by ita task.

Stage 4

This stage still needs to process image data like the stage 3, organize the data into the following form

ocrmt30k
├── ocrmt30k-images
├── image_test.txt
├── image_train.txt
├── image_valid.txt
├── test.tok.lower.en
├── test.tok.lower.zh
├── test.tok.lower.zh-c
├── train.tok.lower.en
├── train.tok.lower.zh
├── train.tok.lower.zh-c
├── valid.tok.lower.en
├── valid.tok.lower.zh
└── valid.tok.lower.zh-c

Run fairseq-preprocess-finetune.sh to handle text data. 2. Run img_feat_extract/get_img_feat.py to extract image feature.

python img_feat_extract/get_img_feat.py \
    --dataset train \
    --model vit_base_patch16_224  \
    --path ../ocrmt30k

Finally, run fairseq-tit-stage4-finetune to finetune the entire model.

Inference

Run fairseq-tit-generate to test final model.

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DeepLearnXMU/mc_tit