  Training generative models that can generate high-quality text with
sufficient diversity is an important open problem for Natural Language
Generation (NLG) community. Recently, generative adversarial models have been
applied extensively on text generation tasks, where the adversarially trained
generators alleviate the exposure bias experienced by conventional maximum
likelihood approaches and result in promising generation quality. However, due
to the notorious defect of mode collapse for adversarial training, the
adversarially trained generators face a quality-diversity trade-off, i.e., the
generator models tend to sacrifice generation diversity severely for increasing
generation quality. In this paper, we propose a novel approach which aims to
improve the performance of adversarial text generation via efficiently
decelerating mode collapse of the adversarial training. To this end, we
introduce a cooperative training paradigm, where a language model is
cooperatively trained with the generator and we utilize the language model to
efficiently shape the data distribution of the generator against mode collapse.
Moreover, instead of engaging the cooperative update for the generator in a
principled way, we formulate a meta learning mechanism, where the cooperative
update to the generator serves as a high level meta task, with an intuition of
ensuring the parameters of the generator after the adversarial update would
stay resistant against mode collapse. In the experiment, we demonstrate our
proposed approach can efficiently slow down the pace of mode collapse for the
adversarial text generators. Overall, our proposed method is able to outperform
the baseline approaches with significant margins in terms of both generation
quality and diversity in the testified domains.
