readme.md

Summary

This is implementation of the paper 'Toward Diverse Text Generation with Inverse Reinforcement Learning' https://arxiv.org/abs/1804.11258 IJCAI2018

Requirement

Python >=2.7.12

Tensorflow >=1.2.0

nltk == 3.2.5

Synthetic

python irl_generation.py

In experiment, we add weight x to entropy term so that it can be used to adjust the balance of 'quality' and 'diversity' of generated texts. The training process of various x is stored in the 'synthetic/save' folder.

x	0.0	0.02	0.04	0.06	0.08	0.085
Result	0.916	0.918	0.914	2.89	3.46	6.91

0.09	0.095	0.1	0.11	0.12	0.14	0.16
4.78	7.36	8.38	7.76	8.24	8.03	8.26

0.25	0.35	0.5	0.75	1.0	1.25	Ground Truth	MLE
8.69	8.86	9.01	9.06	9.12	9.20	5.75	9.03

Image COCO

python irl_coco.py

The results are under 50 epochs pretrain and 50 epochs irl training. Smooth function is not used in calucating BLEU(Same with LeakGAN github repo).

x	0.35	0.45	0.55	0.65	0.75
BLEU-2(f)	0.924	0.889	0.901	0.830	0.827
BLEU-3(f)	0.851	0.789	0.800	0.671	0.647
BLEU-4(f)	0.772	0.690	0.710	0.573	0.556
BLEU-5(f)	0.724	0.624	0.656	0.564	0.571
BLEU-2(b)	0.813	0.851	0.846	0.881	0.883
BLEU-3(b)	0.689	0.733	0.715	0.761	0.754
BLEU-4(b)	0.614	0.657	0.632	0.667	0.652
BLEU-5(b)	0.590	0.627	0.605	0.618	0.606

we also tested with BLEU and Self-BLEU (see TexyGen for details) results are listed here, use same smooth function calculating BLEU in TexyGen github repo:

x	0.35	0.45	0.55	0.65	0.75
BLEU-2	0.922	0.887	0.906	0.828	0.824
BLEU-3	0.844	0.780	0.802	0.653	0.627
BLEU-4	0.751	0.654	0.680	0.463	0.415
BLEU-5	0.645	0.513	0.559	0.308	0.256
Self-BLEU-2	0.936	0.899	0.892	0.829	0.831
Self-BLEU-3	0.881	0.797	0.802	0.641	0.621
Self-BLEU-4	0.829	0.701	0.716	0.464	0.408
Self-BLEU-5	0.781	0.609	0.646	0.325	0.263

Examples of each weight are listed in imagecoco/speech.

Many thanks to SeqGAN and LeakGAN authors, part of my codes are modified from their codes.