  Hyperbolic spaces have proven to be suitable for modeling data of
hierarchical nature. As such we use the Poincare ball to embed sentences with
the goal of proving how hyperbolic spaces can be used for solving Textual
Entailment. To this end, apart from the standard datasets used for evaluating
textual entailment, we developed two additional datasets. We evaluate against
baselines of various backgrounds, including LSTMs, Order Embeddings and
Euclidean Averaging, which comes as a natural counterpart to representing
sentences into the Euclidean space. We consistently outperform the baselines on
the SICK dataset and are second only to Order Embeddings on the SNLI dataset,
for the binary classification version of the entailment task.
