Generating paraphrases, that is, different variations of a sentence conveying
the same meaning, is an important yet challenging task in NLP. Automatically
generating paraphrases has its utility in many NLP tasks like question
answering, information retrieval, conversational systems to name a few...
paper, we introduce iterative refinement of generated paraphrases within VAE
based generation framework. Current sequence generation models lack the
capability to (1) make improvements once the sentence is generated; (2) rectify
errors made while decoding. We propose a technique to iteratively refine the
output using multiple decoders, each one attending on the output sentence
generated by the previous decoder. We improve current state of the art results
significantly - with over 9% and 28% absolute increase in METEOR scores on
Quora question pairs and MSCOCO datasets respectively. We also show
qualitatively through examples that our re-decoding approach generates better
paraphrases compared to a single decoder by rectifying errors and making
improvements in paraphrase structure, inducing variations and introducing new
but semantically coherent information.