  In this paper, we propose a method for obtaining sentence-level embeddings.
While the problem of securing word-level embeddings is very well studied, we
propose a novel method for obtaining sentence-level embeddings. This is
obtained by a simple method in the context of solving the paraphrase generation
task. If we use a sequential encoder-decoder model for generating paraphrase,
we would like the generated paraphrase to be semantically close to the original
sentence. One way to ensure this is by adding constraints for true paraphrase
embeddings to be close and unrelated paraphrase candidate sentence embeddings
to be far. This is ensured by using a sequential pair-wise discriminator that
shares weights with the encoder that is trained with a suitable loss function.
Our loss function penalizes paraphrase sentence embedding distances from being
too large. This loss is used in combination with a sequential encoder-decoder
network. We also validated our method by evaluating the obtained embeddings for
a sentiment analysis task. The proposed method results in semantic embeddings
and outperforms the state-of-the-art on the paraphrase generation and sentiment
analysis task on standard datasets. These results are also shown to be
statistically significant.
