Work

Generating Sentences by Editing Prototypes

Year: 2018

Type: article

Abstract: 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-then-edit 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.

Source: Transactions of the Association for Computational Linguistics

DOI: 10.1162/tacl_a_00030

Authors: Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang

Institution: Stanford University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Defense Advanced Research Projects Agency, Army Research Office

Awards: G5385673730, G7452299184, G8257659198