  Learning sentence vectors that generalise well is a challenging task. In this
paper we compare three methods of learning phrase embeddings: 1) Using LSTMs,
2) using recursive nets, 3) A variant of the method 2 using the POS information
of the phrase. We train our models on dictionary definitions of words to obtain
a reverse dictionary application similar to Felix et al. [1]. To see if our
embeddings can be transferred to a new task we also train and test on the
rotten tomatoes dataset [2]. We train keeping the sentence embeddings fixed as
well as with fine tuning.
