Generative feature matching network (GFMN) is an approach for training state-of-the-art implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.
In sequence-to-sequence models, classical optimal transport (OT) can be applied to semantically match generated sentences with target sentences. However, in non-parallel settings, target sentences are usually unavailable. To tackle this issue without losing the benefits of classical OT, we present a semantic matching scheme based on the Optimal Partial Transport (OPT). Specifically, our approach partially matches semantically meaningful words between source and partial target sequences. To overcome the difficulty of detecting active regions in OPT (corresponding to the words needed to be matched), we further exploit prior knowledge to perform partial matching. Extensive experiments are conducted to evaluate the proposed approach, showing consistent improvements over sequence-to-sequence tasks.