UNIMO

Code for the main conference of ACL 2021 long paper UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning

Abstract

Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e., text or image) or limited multi-modal data (i.e., image-text pairs). In this work, we propose a UNIfied-MOdal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal understanding and generation tasks. Large scale of free text corpus and image collections are utilized to improve the capability of visual and textual understanding, and cross-modal contrastive learning (CMCL) is leveraged to align the textual and visual information into a unified semantic space over a corpus of image-text pairs augmented with related images and texts. With the help of rich non-paired single-modal data, our model is able to learn more generalizable representations, by allowing textual knowledge and visual knowledge to enhance each other in the unified semantic space. The experimental results show that UNIMO greatly improves the performance of several single-modal and multi-modal downstream tasks.

Performance

Results on multi-modal understanding and generation tasks:

Results on single-modal understanding and generation tasks:

TODOs

• [] Add VQA tasks

Dependencies

python 3.7.4
paddlepaddle-gpu==1.8.4.post107
pyrouge==0.1.3 regex==2020.7.14

Pre-trained Models

UNIMO adopts large-scale text corpus, image collections and image-text aligned datasets as the pre-training data. We provide UNIMO pre-trained models below:

UNIMO base (lowercased | 12 layers)

UNIMO-mnli base (lowercased | 12 layers)

UNIMO large (lowercased | 24 layers)

UNIMO-mnli large (lowercased | 24 layers)

MODEL_SIZE=base # base | mnli_base | large | mnli_large
cd /path/to/model_files
wget --no-check-certificate -q https://unimo.bj.bcebos.com/model/unimo_${MODEL_SIZE}_en.tar.gz
tar -zxf unimo_${MODEL_SIZE}_en.tar.gz

Experiments

Our fine-tuning experiments are carried on V100 GPU. The following are the startup methods and basic settings of all downstream tasks:

Text Understanding Tasks

(1) Sentiment Classification

Download SST-2 dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/SST-2.tar.gz
tar -zxf SST.tar.gz

Run the following common to train and evaluate on the SST-2 dataset:

For base model:

bash ./script/classification/SST-2/run.sh

For large model:

bash ./script/classification/SST-2_large/run.sh

Evaluation Results:

(2) Natural Language Inference

Download MNLI-AX dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/MNLI-AX.tar.gz
tar -zxf MNLI-AX.tar.gz

Run the following common to train and evaluate on the MNLI-AX dataset:

For base model:

bash ./script/classification/MNLI-AX/run.sh

For large model:

bash ./script/classification/MNLI-AX_large/run.sh

Evaluation Results:

(3) Similarity Tasks

Download STS-B dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/STS-B.tar.gz
tar -zxf STS-B.tar.gz

Run the following common to train and evaluate on the STS-B dataset:

For base model:

bash ./script/regression/STS-B/run.sh

For large model:

bash ./script/regression/STS-B_large/run.sh

Evaluation Results:

(4) Linguistic Acceptability Judgments

Download CoLA dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/CoLA.tar.gz
tar -zxf CoLA.tar.gz

Run the following common to train and evaluate on the CoLA dataset:

For base model:

bash ./script/classification/CoLA/run.sh

For large model:

bash ./script/classification/CoLA_large/run.sh

Evaluation Results:

Text Generation Tasks

(1) Document Summarization

Download CNN/DailyMail dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/cnndm.tar.gz
tar -zxf cnndm.tar.gz

Download evaluation script:

cd src/eval/tasks
wget --no-check-certificate -q https://unimo.bj.bcebos.com/eval_script/cnndm.tar.gz
tar -zxf cnndm.tar.gz

Run the following common to train and evaluate on the CNN/DailyMail dataset:

For base model:

bash ./script/seq2seq/cnndm/run.sh

For large model:

bash ./script/seq2seq/cnndm_large/run.sh

Evaluation Results:

(2) Sentence Compression

Download Gigaword dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/gigaword.tar.gz
tar -zxf gigaword.tar.gz

Download evaluation script:

cd src/eval/tasks
wget --no-check-certificate -q https://unimo.bj.bcebos.com/eval_script/gigaword.tar.gz
tar -zxf gigaword.tar.gz

Run the following common to train and evaluate on the Gigaword dataset:

For base model:

bash ./script/seq2seq/gigaword/run.sh

For large model:

bash ./script/seq2seq/gigaword_large/run.sh

Evaluation Results:

(3) Question Generation

Download Squad dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/squad_qg.tar.gz
tar -zxf squad_qg.tar.gz

Download evaluation script:

cd src/eval/tasks
wget --no-check-certificate -q https://unimo.bj.bcebos.com/eval_script/squad_qg.tar.gz
tar -zxf squad_qg.tar.gz

Run the following common to train and evaluate on the Squad dataset:

For base model:

bash ./script/seq2seq/squad_qg/run.sh

For large model:

bash ./script/seq2seq/squad_qg_large/run.sh

Evaluation Results:

(4) Conversation Question Answering

Download CoQA dataset:

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/coqa.tar.gz
tar -zxf coqa.tar.gz

Download evaluation script:

cd src/eval/tasks
wget --no-check-certificate -q https://unimo.bj.bcebos.com/eval_script/coqa.tar.gz
tar -zxf coqa.tar.gz

Run the following common to train and evaluate on the CoQA dataset:

For base model:

bash ./script/seq2seq/coqa/run.sh

For large model:

bash ./script/seq2seq/coqa_large/run.sh

Evaluation Results:

Multi-Modal Understanding Tasks

(1) Image-Text Retrieval

Download Flickr30k dataset:

Note: Visual features are extracted by bottom-up-attention

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/Flickr30k.tar.gz # occupies about 37G disk space
tar -zxf Flickr30k.tar.gz

Run the following common to train and evaluate on the Flickr30k dataset:

For base model:

bash ./script/retrieval/Flickr30k/run.sh

For large model:

bash ./script/retrieval/Flickr30k_large/run.sh

Evaluation Results:

Results of Image Retrieval task on Flickr30k dataset

Results of Text Retrieval task on Flickr30k dataset

(2) Visual Entailment

Download SNLI-VE dataset:

Note: Visual features are extracted by bottom-up-attention

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/SNLI-VE.tar.gz
tar -zxf SNLI-VE.tar.gz

Run the following common to train and evaluate on the SNLI-VE dataset:

For base model:

bash ./script/visual_entailment/SNLI-VE/run.sh

For large model:

bash ./script/visual_entailment/SNLI-VE_large/run.sh

Evaluation Results:

Results of Visual Entailment task on SNLI-VE dataset

Multi-Modal Generation Tasks

(1) Image Caption Generation

Download COCO Caption dataset:

Note: Visual features are extracted by bottom-up-attention

cd /path/to/data
wget --no-check-certificate -q https://unimo.bj.bcebos.com/data/coco.tar.gz
tar -zxf coco.tar.gz

Download evaluation script:

cd src/eval/tasks
wget --no-check-certificate -q https://unimo.bj.bcebos.com/eval_script/coco.tar.gz
tar -zxf coco.tar.gz

Run the following common to train and evaluate on the COCO Caption dataset:

For base model:

bash ./script/img2txt/coco/run.sh

For large model:

bash ./script/img2txt/coco_large/run.sh

Evaluation Results:

Citation

If you find our paper and code useful, please cite the following paper:

@article{li2020unimo,
  title={UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning},
  author={Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng},
  journal={arXiv preprint arXiv:2012.15409},
  year={2020}
}

Contact information

For help or issues using UNIMO, please submit a GitHub issue.

For personal communication related to UNIMO, please contact Wei Li (liwei85@baidu.com), Guocheng Niu (niuguocheng@baidu.com) , Can Gao (gaocan01@baidu.com).