Abstract
The recent text generation model using character embeddings is an efficient method for learning high-quality distributed vector representation that captures many precise syntactic and semantic character relationships. In this paper, I present an extension that can be applied to a distributed representation of a database column. Using known column names of a table, we train our model to generate new and meaningful column names.
Type
