README.md

Transformer QG on SQuAD

HLQG is Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.

This is a Reproduce Version

More detail: p208p2002/Transformer-QG-on-SQuAD

Usage

Input Format

C' = [c1, c2, ..., [HL], a1, ..., a|A|, [HL], ..., c|C|]

Input Example

Harry Potter is a series of seven fantasy novels written by British author, [HL]J. K. Rowling[HL].

Who wrote Harry Potter?

Data setting

We report two dataset setting as Follow

SQuAD

• train: 87599\t
• validation: 10570

  SQuAD: 100,000+ Questions for Machine Comprehension of Text

SQuAD NQG

• train: 75722
• dev: 10570
• test: 11877

  Learning to Ask: Neural Question Generation for Reading Comprehension

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