Neural Paraphrase Generation with Stacked Residual LSTM Networks

Understand

In this paper, we propose a novel neural approach for paraphrase generation.

• Conventional para- phrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles.
• To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.
• Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers.