SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection
This repository contains the official PyTorch implementation of the paper "SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection".
In the following, we will guide you how to use this repository step by step.
Multi-modal intent detection aims to utilize various modalities to understand the user’s intentions, which is essential for the deployment of dialogue systems in real-world scenarios. The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data. In this work, we introduce a shallow-to-deep interaction framework with data augmentation (SDIF-DA) to address the above challenges. Firstly, SDIF-DA leverages a shallow-to-deep interaction module to progressively and effectively align and fuse features across text, video, and audio modalities. Secondly, we propose a ChatGPT-based data augmentation approach to automatically augment sufficient training data. Experimental results demonstrate that SDIF-DA can effectively align and fuse multi-modal features by achieving state-of-the-art performance. In addition, extensive analyses show that the introduced data augmentation approach can successfully distill knowledge from the large language model.
Our code is based on PyTorch 1.11 (Cuda version 10.2), required python packages:
- pytorch==1.11.0
- python==3.8.13
- transformers==4.25.1
- wandb==0.14.1
- scikit-learn==1.1.2
- tqdm==4.64.0
We highly suggest you using Anaconda to manage your python environment. If so, you can run the following command directly on the terminal to create the environment:
conda create -n env_name python=3.8.13
source activate env_name
pip install -r requirements.txt
The text data and our augmentation data are already put in data/MIntRec, for the video and audio data, download the MIntRec dataset: Google Drive or BaiduYun Disk (code：95lo), and put audio data audio_feats.pkl, video data video_feats.pkl into path data/MIntRec
The script run.py acts as a main function to the project, you can run the experiments by the following commands.
# Binary-class
python run.py --gpu_id '0' --train --config_file_name 'sdif_bi.py' > SDIF_DA_binary.log 2>&1 &
# Twenty-class
python run.py --gpu_id '0' --train --config_file_name 'sdif.py' > SDIF_DA_twenty.log 2>&1 & If you find this project useful for your research, please consider citing the following paper:
@misc{huang2023sdifda,
title={SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection},
author={Shijue Huang and Libo Qin and Bingbing Wang and Geng Tu and Ruifeng Xu},
year={2023},
eprint={2401.00424},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
If you have any question, please issue the project or email Shijue Huang and we will reply you soon.