TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Text Generation	Chinese Poems	SeqGAN	BLEU-2	0.738	# 2
Text Generation	COCO Captions	SeqGAN	BLEU-2	0.831	# 5
Text Generation	COCO Captions	SeqGAN	BLEU-3	0.642	# 5
Text Generation	COCO Captions	SeqGAN	BLEU-4	0.521	# 3
Text Generation	COCO Captions	SeqGAN	BLEU-5	0.427	# 4
Text Generation	EMNLP2017 WMT	SeqGAN	BLEU-2	0.859	# 3
Text Generation	EMNLP2017 WMT	SeqGAN	BLEU-3	0.6015	# 3
Text Generation	EMNLP2017 WMT	SeqGAN	BLEU-4	0.4541	# 3
Text Generation	EMNLP2017 WMT	SeqGAN	BLEU-5	0.4498	# 3

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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

18 Sep 2016 • Lantao Yu • Wei-Nan Zhang • Jun Wang • Yong Yu

As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens... (read more)

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Ranked #3 on Text Generation on Chinese Poems

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Text Generation	Chinese Poems	SeqGAN	BLEU-2	0.738	# 2	Compare
Text Generation	COCO Captions	SeqGAN	BLEU-2	0.831	# 5	Compare
			BLEU-3	0.642	# 5	Compare
			BLEU-4	0.521	# 3	Compare
			BLEU-5	0.427	# 4	Compare
Text Generation	EMNLP2017 WMT	SeqGAN	BLEU-2	0.859	# 3	Compare
			BLEU-3	0.6015	# 3	Compare
			BLEU-4	0.4541	# 3	Compare
			BLEU-5	0.4498	# 3	Compare

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METHOD	TYPE
Convolution	Convolutions
GAN	Generative Models

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

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