基于SGAN的中文问答生成研究

生成对抗网络GAN(Generative adversarial networks)仅适用于解决连续型数据,同时中文对话模型训练缺乏高质量的样本数据集。研究开放域中文闲聊的问答生成,对话文本是离散型数据,GAN的使用受到限制。设计新的序列对抗生成网络SGAN(Sequence GAN)来解决此问题。SGAN使用基于强化学习的生成器扩展GAN,可以解决序列生成问题。同时使用Actor-Critic策略梯度训练模型,评价指标采用精准度和召回率。实验结果表明,该对话序列对抗模型能够生成足够的对话样本混淆人为提供的样本。

CHINESE QUESTION ANSWER GENERATION BASED ON SGAN

Generating antagonistic network(GAN) is only suitable for solving continuous data, while Chinese dialogue model training lacks high-quality sample data sets. This paper has a study on the Chinese question and answer generation of open domain. However, dialog text is discrete data, so the use of GAN is limited. Therefore, we designed a new model called SGAN (sequence GAN) to solve these problems. SGAN extended the GAN by using a method called reinforcement learning to train the generator to solve the problem of sequence generation. SGAN also used a policy gradient called actor-critic to train the networks. The precision and recall rate were used as the evaluation indexes of the model. Experimental results show that the proposed dialogue sequence adversarial model can generate enough dialogue samples to confuse the artificial-provided samples.