Despiting the linear structured left-branching and rightbranching tree encoders, we find that, tree-based encoders generally perform better than Bi-LSTMs on tasks of sentence relation and sentence generation, which may require relatively more long term context features for obtaining better performances.
the improvements of tree encoders on NLI and Para are relatively small, which may be caused by that sentences of the two tasks are shorter than others, and the tree encoder does not get enough advantages to capture long-term context in short sentences.