- AACL-IJCNLP 2022 Findings Paper
- Our code is based on ConvDR and ORConvQA.
- It is recommended to create a conda environment for the project with
conda create -n conadr_qa python=3.8
- Install dependencies
# Install torch (please check your CUDA version)
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu102
git clone https://github.com/MiuLab/ConvADR-QA.git
cd ConvADR-QA
pip install -r requirements.txt
- Download OR-QuAC data
bash scripts/download.sh
- Preprocessing
bash scripts/preprocessing.sh
- Document Embedding Geneation
bash scripts/gen_embed.sh
Note that this step could take a lot of memory and time.
- To use ranking loss, we first need to find negative documents using manual queries:
bash scripts/run_train.sh find_neg
- After the retrieval finishes, we can select negative documents using the following script:
bash scripts/run_train.sh gen_rank
- Model training:
bash scripts/run_train.sh train
bash scripts/run_inference.sh
@inproceedings{fang2022open,
title={Open-Domain Conversational Question Answering with Historical Answers},
author={Fang, Hung-Chieh and Hung, Kuo-Han and Huang, Chen-Wei and Chen, Yun-Nung},
booktitle={Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022},
pages={319--326},
year={2022}
}