BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation
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In many natural language generation tasks, incorporating additional knowledge like lexical constraints into the model's output is significant, which take the form of phrases or words that must be present in the output sequence. Unfortunately, existing neural language model cannot be used directly to generate lexically constrained sentences. In this paper, we propose a new algorithmic framework called BFGAN to address this challenge. We employ a backward generator and a forward generator to generate lexically constrained sentence together, and use a discriminator to guide the joint training of two generators by assigning them reward signals. Experimental results on automatic and human evaluation demonstrate significant improvements over previous baselines.
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