Among the most enticing things about natural language is the fact that it can be written and spoken in myriad ways. The act of paraphrasing, or saying something that has the same meaning as the original, but in a slightly different manner, has a plethora of possible applications, for e.g. sentence simplification, complication, reporting, vocabulary modulation, tone regulation. We provide a novel deep generative model for paraphrase generation using a variational auto-encoder. We also suggest a method for controlled paraphrase generation using pre-fixed latent variables. We obtain promising results on machine translation & paraphrase evaluation metrics and some interesting correlations in human evaluation. Our results are qualitatively and quantitatively comparable. We also report the current issues with paraphrase evaluation methods, and analyze the popular datasets in that respect.
Spoken and written content can be formulated in myriad ways to make it accessible to a diverse set of audiences. A classic by Charles Dickens would need its sentences to be expressed in a simpler format for children to be able to understand it. On the other hand, a children’s story would need to be adapted in a more mature language with a wider variety in sentence structure and vocabulary in order to be appealing for adult readers. Reminders and queries can be rephrased to make them seem more actionable. Computer agents communicating with humans can use multiple wordings of the same intent to elicit the desired response from the interacting person. These demands can be fulfilled by an efficient paraphrase generation system, if trained on a proper dataset. Deep learning has found applications in a variety of domains in the recent years. The availability of massive datasets and plenty of computational resources have been instrumental in aiding deep learning algorithms to find solutions to problems that were a long shot earlier. Its influence on problems in natural language processing has been immense as well, right from state of the art sentiment classifiers and spreading its net wide enough to entail impressive language understanding and generation techniques. Paraphrase generation is one of the many sequence to sequence generation tasks that have been tackled by deep learning. Although decent models exist, much work remains before we can arrive at industry grade paraphrasers that are expressive in and adaptable to a wide range of domains. Probabilistic modeling of classification tasks has existed since decades, but its combination with deep neural nets has led to a recent surge in innovations leading up to deep generative models. Variational Autoencoders (Kingma and Welling, 2013) and Generative Adversarial Networks (Goodfellow et al., 2014) have been some of the more popular models. While their utility and applications in natural language tasks is being studied, a flurry of work on VAE modifications to be adaptable to sequence to sequence learning has appeared over the last couple of years. Solving the problem of paraphrase generation using variational autoencoders thus seemed like an interesting idea for a project, and we went ahead with it. The recent work by (Gupta et al., 2017) has been a guiding light, since it has tackled the same problem, though with a different architecture.