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Found 12 papers, 6 papers with code
FiLM: Fill-in Language Models for Any-Order Generation
Language models have become the backbone of today's AI systems.
Generating Sequences by Learning to Self-Correct
Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content.
Controlling Directions Orthogonal to a Classifier
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer.
Text Style Transfer with Confounders
Existing methods for style transfer operate either with paired sentences or distributionally matched corpora which differ only in the desired style.
Blank Language Models
We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks.
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.
Diverse Machine Translation with a Single Multinomial Latent Variable
There are many ways to translate a sentence into another language.
Mixture Models for Diverse Machine Translation: Tricks of the Trade
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Style Transfer from Non-Parallel Text by Cross-Alignment
We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
Ranked #7 on
Text Style Transfer
on Yelp Review Dataset (Small)
