Generating Sentences by Editing Prototypes

Autor: Tatsunori Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang
Rok vydání: 2017
Předmět:
FOS: Computer and information sciences
Linguistics and Language
Computer Science - Machine Learning
Perplexity
Computer science
Computer Science - Artificial Intelligence
media_common.quotation_subject
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
Machine Learning (stat.ML)
02 engineering and technology
computer.software_genre
Semantics
ComputingMethodologies_ARTIFICIALINTELLIGENCE
Machine Learning (cs.LG)
Artificial Intelligence
Statistics - Machine Learning
020204 information systems
0202 electrical engineering
electronic engineering
information engineering

Quality (business)
Neural and Evolutionary Computing (cs.NE)
media_common
computer.programming_language
Computer Science - Computation and Language
business.industry
Communication
Sampling (statistics)
Computer Science - Neural and Evolutionary Computing
Computer Science Applications
Human-Computer Interaction
Artificial Intelligence (cs.AI)
Scratch
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
020201 artificial intelligence & image processing
Language model
Artificial intelligence
business
computer
Computation and Language (cs.CL)
Natural language processing
Generative grammar
Sentence
DOI: 10.48550/arxiv.1709.08878
Popis: We propose a new generative model of sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional 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.
Comment: 14 pages, Transactions of the Association for Computational Linguistics (TACL), 2018
Databáze: OpenAIRE