# Transformer QG on SQuAD
The inputs of the model refers to 
```
we integrate C and A into a new C' in the following form.
C' = [c1, c2, ..., [HL], a1, ..., a|A|, [HL], ..., c|C|]
```
> Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)

More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQuAD)

## Features
- Fully pipline from fine-tune to evaluation
- Support most of state of the art models
- Fast deploy as a API server

## Data setting
We report two dataset setting as Follow

### SQuAD
- train: 87599	
- validation: 10570
> [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250)

### SQuAD NQG
- train: 75722
- dev: 10570
- test: 11877
> [Learning to Ask: Neural Question Generation for Reading Comprehension](https://arxiv.org/abs/1705.00106)

## Available models
- BART
- GPT2
- T5

## Expriments
We report score with `NQG Scorer` which is using in SQuAD NQG.

If not special explanation, the size of the model defaults to "base".

### SQuAD
Model                            |Bleu 1|Bleu 2|Bleu 3|Bleu 4|METEOR|ROUGE-L|
---------------------------------|------|------|------|------|------|-------|
BART-HLSQG                       |54.67 |39.26 |30.34 |24.15 |25.43 |52.64  |
GPT2-HLSQG                       |49.31 |33.95 |25.41| 19.69 |22.29 |48.82  |
T5-HLSQG                         |54.29 |39.22 |30.43 |24.26 |25.56 |53.11  |

### SQuAD NQG
Model                            |Bleu 1|Bleu 2|Bleu 3|Bleu 4|METEOR|ROUGE-L|
---------------------------------|------|------|------|------|------|-------|
BERT-HLSQG (Chan et al.)         |49.73 |34.60 |26.13 |20.33 |23.88 |48.23  |
BART-HLSQG                       |54.12 |38.19 |28.84 |22.35 |24.55 |51.03  |
GPT2-HLSQG                       |49.82 |33.69 |24.71 |18.63 |21.90 |47.60  |
T5-HLSQG                         |53.13 |37.60 |28.62 |22.38 |24.48 |51.20  |