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. NTI constructs a full n-ary tree
by processing the input text with its node function in a bottom-up fashion.
Attention mechanism can then be applied to both structure and node function. We
implemented and evaluated a binarytree model of NTI, showing the model achieved
the state-of-the-art performance on three different NLP tasks: natural language
inference, answer sentence selection, and sentence classification,
outperforming state-of-the-art recurrent and recursive neural networks.

本文提出了一种不依赖于语法分析的树形模型，名为 Neural Tree Indexers，该模型可以以自底而上的方式处理输入的文本，从而构建完整的 n 叉树，其中包括结构和节点功能，借助注意机制，该模型在自然语言推理、答案句子选择和句子分类等三个不同的 NLP 任务上的表现超越了现有的 RNN 和递归神经网络。