NOTE

Due to certain bugs in the evaluation metric (AP) used in the original experiment, the results for visual sarcasm target identification in the paper tend to be overly idealized. Therefore, we kindly request that researchers refrain from referencing the experimental results related to visual sarcasm target identification in the paper. More explanation is coming soon.

Multimodal Sarcasm Target Identification in Tweets

Environment

Python packages

• python==3.7
• torch==1.8.0
• gensim==3.8.0
• numpy==1.18.3
• torchcrf==1.0.4
• pytorch-pretrained-bert==0.6.2 (pip install -r requirements.txt)

Configuration

All configuration are listed in main.py. Please verify parameters before running the codes.

Data

datasets/
├── images/ 
├── Visual target labels/
└── Textual target labels/
    ├──train
    ├──val
    └──test

####The MSTI datasets format is as follows:

IMGID:9737
nice	O
warm	O
running	O
weather	B-S
this	O
morning	O
...	O

IMGID:9516
andy	B-S
murray	I-S
thrown	O
out	O
of	O
#	O
australianopen	O
after	O
celebrating	O
his	O
win	O
.	O

Pre-trained Models

pretrained/
├── bert-base-uncased/
│   ├── vocab.txt
│   ├── bert_config.json
│   └── pytorch_model.bin
├── bert-large-uncased/
│   ├── vocab.txt
│   ├── bert_config.json
│   └── pytorch_model.bin
└── yolo/

Usage

Training

• python main.py

Testing

• python test.py