Neural Tree Indexers for Text Understanding

Understand

Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens.

• In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language.
• However, the current recursive architecture is limited by its dependence on syntactic tree.
• In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic treebased recursive models.