HVPNet

Code for the NAACL2022 (Findings) paper "Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction".

Model Architecture

The overall architecture of our hierarchical modality fusion network.

Requirements

To run the codes, you need to install the requirements:

pip install -r requirements.txt

Data Preprocess

To extract visual object images, we first use the NLTK parser to extract noun phrases from the text and apply the visual grouding toolkit to detect objects. Detailed steps are as follows:

1. Using the NLTK parser (or Spacy, textblob) to extract noun phrases from the text.
2. Applying the visual grouding toolkit to detect objects. Taking the twitter2015 dataset as an example, the extracted objects are stored in twitter2015_aux_images. The images of the object obey the following naming format: imgname_pred_yolo_crop_num.png, where imgname is the name of the raw image corresponding to the object, num is the number of the object predicted by the toolkit. (Note that in train/val/test.txt, text and raw image have a one-to-one relationship, so the imgname can be used as a unique identifier for the raw images)
3. Establishing the correspondence between the raw images and the objects. We construct a dictionary to record the correspondence between the raw images and the objects. Taking twitter2015/twitter2015_train_dict.pth as an example, the format of the dictionary can be seen as follows: {imgname:['imgname_pred_yolo_crop_num0.png', 'imgname_pred_yolo_crop_num1.png', ...] }, where key is the name of raw images, value is a List of the objects.

The detected objects and the dictionary of the correspondence between the raw images and the objects are available in our data links.

Data Download

• Twitter2015 & Twitter2017

  The text data follows the conll format. You can download the Twitter2015 data via this link and download the Twitter2017 data via this link. Please place them in data/NER_data.

  You can also put them anywhere and modify the path configuration in run.py

• MNRE

  The MNRE dataset comes from MEGA, many thanks.

  You can download the MRE dataset with detected visual objects from Google Drive or use the following commands:

  cd data
  wget 120.27.214.45/Data/re/multimodal/data.tar.gz
  tar -xzvf data.tar.gz
  mv data RE_data

The expected structure of files is:

HMNeT
 |-- data
 |    |-- NER_data
 |    |    |-- twitter2015  # text data
 |    |    |    |-- train.txt
 |    |    |    |-- valid.txt
 |    |    |    |-- test.txt
 |    |    |    |-- twitter2015_train_dict.pth  # {imgname: [object-image]}
 |    |    |    |-- ...
 |    |    |-- twitter2015_images       # raw image data
 |    |    |-- twitter2015_aux_images   # object image data
 |    |    |-- twitter2017
 |    |    |-- twitter2017_images
 |    |    |-- twitter2017_aux_images
 |    |-- RE_data
 |    |    |-- img_org          # raw image data
 |    |    |-- img_vg           # object image data
 |    |    |-- txt              # text data
 |    |    |-- ours_rel2id.json # relation data
 |-- models	# models
 |    |-- bert_model.py
 |    |-- modeling_bert.py
 |-- modules
 |    |-- metrics.py    # metric
 |    |-- train.py  # trainer
 |-- processor
 |    |-- dataset.py    # processor, dataset
 |-- logs     # code logs
 |-- run.py   # main 
 |-- run_ner_task.sh
 |-- run_re_task.sh

Train

NER Task

The data path and GPU related configuration are in the run.py. To train ner model, run this script.

bash run_twitter15.sh
bash run_twitter17.sh

RE Task

To train re model, run this script.

bash run_re_task.sh

Test

NER Task

To test ner model, you can use the tained model and set load_path to the model path, then run following script:

python -u run.py \
      --dataset_name="twitter15/twitter17" \
      --bert_name="bert-base-uncased" \
      --seed=1234 \
      --only_test \
      --max_seq=80 \
      --use_prompt \
      --prompt_len=4 \
      --sample_ratio=1.0 \
      --load_path='your_ner_ckpt_path'

RE Task

To test re model, you can use the tained model and set load_path to the model path, then run following script:

python -u run.py \
      --dataset_name="MRE" \
      --bert_name="bert-base-uncased" \
      --seed=1234 \
      --only_test \
      --max_seq=80 \
      --use_prompt \
      --prompt_len=4 \
      --sample_ratio=1.0 \
      --load_path='your_re_ckpt_path'

Acknowledgement

The acquisition of Twitter15 and Twitter17 data refer to the code from UMT, many thanks.

The acquisition of MNRE data for multimodal relation extraction task refer to the code from MEGA, many thanks.

Papers for the Project & How to Cite

If you use or extend our work, please cite the paper as follows:

@inproceedings{DBLP:conf/naacl/ChenZLYDTHSC22,
  author    = {Xiang Chen and
               Ningyu Zhang and
               Lei Li and
               Yunzhi Yao and
               Shumin Deng and
               Chuanqi Tan and
               Fei Huang and
               Luo Si and
               Huajun Chen},
  editor    = {Marine Carpuat and
               Marie{-}Catherine de Marneffe and
               Iv{\'{a}}n Vladimir Meza Ru{\'{\i}}z},
  title     = {Good Visual Guidance Make {A} Better Extractor: Hierarchical Visual
               Prefix for Multimodal Entity and Relation Extraction},
  booktitle = {Findings of the Association for Computational Linguistics: {NAACL}
               2022, Seattle, WA, United States, July 10-15, 2022},
  pages     = {1607--1618},
  publisher = {Association for Computational Linguistics},
  year      = {2022},
  url       = {https://doi.org/10.18653/v1/2022.findings-naacl.121},
  doi       = {10.18653/v1/2022.findings-naacl.121},
  timestamp = {Tue, 23 Aug 2022 08:36:33 +0200},
  biburl    = {https://dblp.org/rec/conf/naacl/ChenZLYDTHSC22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}