This is the project containing source code for the paper S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering in ACL 2023.
python==3.7
torch==1.7.1+cu110
transformers==4.21.1
Download all data from hear .
Then unzip Data.zip
.
Download bert-base-uncased model
, deberta-base model
, bart-large model
from huggingfacehub. Or you can use them directly without downloading the model by changing the code.
Put bert-base-uncased model
in ./PTM/bert-base-uncased
and bart-large model
in ./PTM/bart-large
.
If your work just focuses on the reader rather than retrieval.
Directly use train.row.json
, dev.row.json
and test.row.json
for your experiments.
If you want to get final answers of dev or test set.
First, download reader checkpoint from hear.
Then you can directly run bash read_dev.sh
or bash read_test.sh
to get the answers.
retriever step1 bash retrieve1.sh
retriever step2 bash retrieve2.sh
bash read.sh