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Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net
We propose a novel siamese generative adversarial net for abstractive text summarization (SSPGAN), which can preserve the main semantics of the source text.
CoCGAN: Contrastive Learning for Adversarial Category Text Generation
The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples.
Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation
The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years.
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