DiaASQ

This repository contains code (will be available soon) and data for our paper DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis

To clone the repository, please run the following command:

git clone https://github.com/unikcc/DiaASQ

Quick Links

• Overview

• Requirements

• Data Preparation

• Citation

Overview

In this work, we propose a new task named DiaASQ, which aims to extract Target-Aspect-Opinion-Sentiment quadruples from the given dialogue. You can find more details in our paper.

Requirements

The model is implemented using PyTorch. The versions of the main packages used are shown below.

• python>=3.8
• attrdict>=2.0.1
• jieba>=0.42.1
• PyYAML>=6.0
• spacy>=3.4.2
• torch>=1.8.1

To set up the dependencies, you can run the following command:

pip install -r requirements.txt

Data Preparation

You can download the source data from Google Drive Link

Then, unzip the files and place them under the data directory like the following:

./data/dataset/annotation_zh
./data/dataset/annotation_en

Generate JSON format files for Chinese data and English data(You should download ``)

python prepare_data.py --lang zh
python prepare_data.py --lang en

For example, the Chinese version train dataset with JSON format should locate at:

./data/dataset/json_zh/train.json

Citation

If you want to use our dataset, please cite the following paper:

@article{lietal2022arxiv,
  title={DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis},
  author={Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji}
  journal={arXiv preprint arXiv:2211.05705},
  year={2022}
}