The dataset contains 9,026 questions and 870 dialogs. A more detailed statistics of the dataset is presented in the below table. The Paper is currently released on arXiv [Paper Link].
The statistics also demnstrate a comparison with English language datasets of QuAC and CoQA.
| PCoQA | CoQA | QuAC | |
|---|---|---|---|
| documents | 870 | 8,399 | 11,568 |
| questions | 9,026 | 127,000 | 86,568 |
| questions / dialog | 10.4 | 15.2 | 7.2 |
| unanswerable rate | 15.7 | 1.3 | 20.2 |
We have tested two clique of models:
- Baseline Models: ParsBert & XML-Roberta are used.
- Pre-trained Models: Baseline models are pre-trained on PaeSQuAD and QuAC before being finetuned. In the below table X + Y shows utilizing Y which is pre-trained on X.
| Model | EM | F1 | HEQ-Q | HEQ-M | HEQ-D |
|---|---|---|---|---|---|
| ParsBERT | 21.82 | 37.06 | 30.70 | 0.0 | 0.0 |
| XLM-Roberta | 30.47 | 47.78 | 39.51 | 2.45 | 1.63 |
| ParSQuAD + ParsBERT | 21.74 | 40.48 | 31.95 | 0.8 | 0.0 |
| QuAC + XLM-Roberta | 32.81 | 51.66 | 43.10 | 3.27 | 1.63 |
| ParSQuAD + XLM-Roberta | 35.93 | 53.75 | 46.21 | 1.63 | 0.8 |
| Human | 85.50 | 86.97 | - | - | - |
In the below picure, the mean of F1 along different methods are shown.
In the Code directory, .ipynb files are for pre-training the transformers. run run_PCoQA.py file according to your desired settings to obtain the results. You can run it like:
python run_PCoQA.py --model parsbert --pretrained_dataset none --hist_num 2 --do_test