  Many state-of-art neural models designed for monotonicity reasoning perform
poorly on downward inference. To address this shortcoming, we developed an
attentive tree-structured neural network. It consists of a tree-based
long-short-term-memory network (Tree-LSTM) with soft attention. It is designed
to model the syntactic parse tree information from the sentence pair of a
reasoning task. A self-attentive aggregator is used for aligning the
representations of the premise and the hypothesis. We present our model and
evaluate it using the Monotonicity Entailment Dataset (MED). We show and
attempt to explain that our model outperforms existing models on MED.
