  In Natural Language Processing (NLP), we often need to extract information
from tree topology. Sentence structure can be represented via a dependency tree
or a constituency tree structure. For this reason, a variant of LSTMs, named
Tree-LSTM, was proposed to work on tree topology. In this paper, we design a
generalized attention framework for both dependency and constituency trees by
encoding variants of decomposable attention inside a Tree-LSTM cell. We
evaluated our models on a semantic relatedness task and achieved notable
results compared to Tree-LSTM based methods with no attention as well as other
neural and non-neural methods and good results compared to Tree-LSTM based
methods with attention.
