Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Tree structures are commonly used in the tasks of semantic analysis and understanding over the data of different modalities, such as natural language, 2D or 3D graphics and images, or Web pages. Previous studies model the tree structures in a bottom-up manner, where the leaf nodes (given in advance) are merged into internal nodes until they reach the root node. However, these models are not applicable when the leaf nodes are not explicitly specified ahead of prediction. Here, we introduce a neural machine for top-down generation of tree structures that aims to infer such tree structures without the specified leaf nodes. In this model, the history memories from ancestors are fed to a node to generate its (ordered) children in a recursive manner. This model can be utilized as a tree-structured decoder in the framework of "X to tree" learning, where X stands for any structure (e.g. chain, tree etc.) that can be represented as a latent vector. By transforming the dialogue generation problem into a sequence-to-tree task, we demonstrate the proposed X2Tree framework achieves a 11.15% increase of response acceptance ratio over the baseline methods.