DiaASQ

This repository contains data and code for the ACL23 (findings) paper: DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis

Also see the project page for more details.

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To clone the repository, please run the following command:

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

News 🎉

📢 2023-05-10: Released code and dataset.
⚡ 2022-12-10: Created repository.

Quick Links

• Overview
• Requirements
• Code Usage
• 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. More details about the task can be found in our paper.

Requirements

The model is implemented using PyTorch. The versions of the main packages:

• python>=3.7
• torch>=1.8.1

Install the other required packages:

pip install -r requirements.txt

Code Usage

• Dataset: the dataset can be found at:

  data/dataset

• Train && Evaluate on the Chinese dataset

  bash scripts/train_zh.sh

• Train && Evaluate on the English dataset

  bash scripts/train_en.sh

• GPU memory requirements

  Dataset	Batch size	GPU Memory
  Chinese	2	8GB.
  English	2	16GB.

• Customized hyperparameters:
  You can set hyperparameters in main.py or src/config.yaml, and the former has a higher priority.

Citation

If you 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}
}