  Paraphrase detection is an important task in text analytics with numerous
applications such as plagiarism detection, duplicate question identification,
and enhanced customer support helpdesks. Deep models have been proposed for
representing and classifying paraphrases. These models, however, require large
quantities of human-labeled data, which is expensive to obtain. In this work,
we present a data augmentation strategy and a multi-cascaded model for improved
paraphrase detection in short texts. Our data augmentation strategy considers
the notions of paraphrases and non-paraphrases as binary relations over the set
of texts. Subsequently, it uses graph theoretic concepts to efficiently
generate additional paraphrase and non-paraphrase pairs in a sound manner. Our
multi-cascaded model employs three supervised feature learners (cascades) based
on CNN and LSTM networks with and without soft-attention. The learned features,
together with hand-crafted linguistic features, are then forwarded to a
discriminator network for final classification. Our model is both wide and deep
and provides greater robustness across clean and noisy short texts. We evaluate
our approach on three benchmark datasets and show that it produces a comparable
or state-of-the-art performance on all three.
