ReCO

ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion

Data

Dataset is available at https://drive.google.com/drive/folders/1rOAoKcLhMhge9uVQFM2_D1EU0AjnpWFa?usp=sharing

download the data and put the json files to the data/ReCO directory

Stats

Requirenments

transformers
torch>=1.3.0
tqdm
joblib
apex(for mixed-precision training)

Train and Test

For BiDAF and other types of model, you can go to the BiDAF folder and run. But the result is somewhat low _

Pre-training methods finetuning:

For single node training:
python3 train.py --model_type=bert-base-chinese
for multiple nodes distributed training:
python3 -m torch.distributed.launch --nproc_per_node=8 train.py --model_type=bert-base-chinese

If you want to use the original doc as the context, you can set the clean(one['passage']) in prepare_data.py line 29 to clean(one['doc']).

model card

Test

python3 test.py --model_type=bert-base-chinese

Results

Citation

If you use ReCO in your research, please cite our work with the following BibTex Entry

@inproceedings{DBLP:conf/aaai/WangYZXW20,
  author    = {Bingning Wang and
               Ting Yao and
               Qi Zhang and
               Jingfang Xu and
               Xiaochuan Wang},
  title     = {ReCO: {A} Large Scale Chinese Reading Comprehension Dataset on Opinion},
  booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
               2020, The Thirty-Second Innovative Applications of Artificial Intelligence
               Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
               Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
               February 7-12, 2020},
  pages     = {9146--9153},
  publisher = {{AAAI} Press},
  year      = {2020},
  url       = {https://aaai.org/ojs/index.php/AAAI/article/view/6450},
  timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},
  biburl    = {https://dblp.org/rec/conf/aaai/WangYZXW20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}