May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation

Understand

Recent neural sequence-to-sequence models with a copy mechanism have achieved remarkable progress in various text generation tasks.

• These models addressed out-of-vocabulary problems and facilitated the generation of rare words.
• However, the identification of the word which needs to be copied is difficult, as observed by prior copy models, which suffer from incorrect generation and lacking abstractness.
• In this paper, we propose a novel supervised approach of a copy network that helps the model decide which words need to be copied and which need to be generated.