  Sentence embedding is an effective feature representation for most deep
learning-based NLP tasks. One prevailing line of methods is using recursive
latent tree-structured networks to embed sentences with task-specific
structures. However, existing models have no explicit mechanism to emphasize
task-informative words in the tree structure. To this end, we propose an
Attentive Recursive Tree model (AR-Tree), where the words are dynamically
located according to their importance in the task. Specifically, we construct
the latent tree for a sentence in a proposed important-first strategy, and
place more attentive words nearer to the root; thus, AR-Tree can inherently
emphasize important words during the bottom-up composition of the sentence
embedding. We propose an end-to-end reinforced training strategy for AR-Tree,
which is demonstrated to consistently outperform, or be at least comparable to,
the state-of-the-art sentence embedding methods on three sentence understanding
tasks.
