Structural Information Preserving for Graph-to-Text Generation

Understand

The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs.

• As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs.
• We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information.
• In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a.