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Adversarial Mutual Information for Text Generation
Boyuan Pan · Yazheng Yang · Kaizhao Liang · Bhavya Kailkhura · Zhongming Jin · Xian-Sheng Hua · Deng Cai · Bo Li

Wed Jul 15 08:00 AM -- 08:45 AM & Wed Jul 15 07:00 PM -- 07:45 PM (PDT) @

Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little attention to modeling the backward network of MI (i.e., dependency from the target to the source), which is crucial to the tightness of the variational information maximization lower bound. In this paper, we propose Adversarial Mutual Information (AMI): a text generation framework which is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between the source and target. Within this framework, the forward and backward networks are able to iteratively promote or demote each other's generated instances by comparing the real and synthetic data distributions. We also develop a latent noise sampling strategy that leverages random variations at the high-level semantic space to enhance the long term dependency in the generation process. Extensive experiments based on different text generation tasks demonstrate that the proposed AMI framework can significantly outperform several strong baselines, and we also show that AMI has potential to lead to a tighter lower bound of maximum mutual information for the variational information maximization problem.

Author Information

Boyuan Pan (Zhejiang University)
Yazheng Yang (Zhejiang University)
Kaizhao Liang (University of Illinois, Urbana Champaign)

Class 2020 CS@UIUC, ML engineer@ SambaNova starting this July.

Bhavya Kailkhura (Lawrence Livermore National Laboratory)
Zhongming Jin (Alibaba Group)
Xian-Sheng Hua (Alibaba Group)
Deng Cai (ZJU)
Bo Li (UIUC)

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