Paraphrasing (computational linguistics): Difference between revisions

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=== Long short-term memory ===

There has been success in using [[long short-term memory]] (LSTM) models to generate paraphrases.<ref name=Prakash>{{Citation|last1=Prakash|first1=Aaditya|last2=Hasan|first2=Sadid A.|last3=Lee|first3=Kathy|last4=Datla|first4=Vivek|last5=Qadir|first5=Ashequl|last6=Liu|first6=Joey|last7=Farri|first7=Oladimeji|title=Neural Paraphrase Generation with Staked Residual LSTM Networks|year=2016|arxiv=1610.03098|bibcode=2016arXiv161003098P}}</ref> In short, the model consists of an encoder and decoder component, both implemented using variations of a stacked [[Vanishing gradient problem#Residual networks|residual]] LSTM. First, the encoding LSTM takes a [[one-hot]] encoding of all the words in a sentence as input and produces a final hidden vector, which can be viewed as a representation of the input sentence. The decoding LSTM then takes the hidden vector as input and generates new sentence, terminating in an end-of-sentence token. The encoder and decoder are trained to take a phrase and reproduce the one-hot distribution of a corresponding paraphrase by minimizing [[perplexity]] using simple [[stochastic gradient descent]]. New paraphrases are generated by inputting a new phrase to the encoder and passing the output to the decoder.

The introduction of Artificial Intelligence and Natural language processing improved the paraphrased output considarably So many platforms likes of Machinewrite https://www.machinewrites.com/paraphrasing are leveraging the latest technology.

== Paraphrase recognition ==