We propose a new generative language model for sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional language models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-thenedit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies. Sample from the training set Edit using a�en�on The food here is ok but not worth the price . The food is mediocre and not worth the ridiculous price . The food is good but not worth the horrible customer service . The food here is not worth the drama . The food is not worth the price .
REF propose a new generative model that first samples a prototype sentence from the training corpus and then edits it into a new sentence.
Generating Sentences by Editing Prototypes
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science Mathematics