gpt2-squad-nqg-hl – AI Model by p208p2002 | AlphaNeural AI

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p208p2002/
gpt2-squad-nqg-hl

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text-generation

question-generation

autotrain_compatible

text-generation-inference

endpoints_compatible

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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