  Semantic Similarity between two sentences can be defined as a way to
determine how related or unrelated two sentences are. The task of Semantic
Similarity in terms of distributed representations can be thought to be
generating sentence embeddings (dense vectors) which take both context and
meaning of sentence in account. Such embeddings can be produced by multiple
methods, in this paper we try to evaluate LSTM auto encoders for generating
these embeddings. Unsupervised algorithms (auto encoders to be specific) just
try to recreate their inputs, but they can be forced to learn order (and some
inherent meaning to some extent) by creating proper bottlenecks. We try to
evaluate how properly can algorithms trained just on plain English Sentences
learn to figure out Semantic Similarity, without giving them any sense of what
meaning of a sentence is.
