  Learning distributed sentence representations remains an interesting problem
in the field of Natural Language Processing (NLP). We want to learn a model
that approximates the conditional latent space over the representations of a
logical antecedent of the given statement. In our paper, we propose an approach
to generating sentences, conditioned on an input sentence and a logical
inference label. We do this by modeling the different possibilities for the
output sentence as a distribution over the latent representation, which we
train using an adversarial objective. We evaluate the model using two
state-of-the-art models for the Recognizing Textual Entailment (RTE) task, and
measure the BLEU scores against the actual sentences as a probe for the
diversity of sentences produced by our model. The experiment results show that,
given our framework, we have clear ways to improve the quality and diversity of
generated sentences.
