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Unsupervised Natural Language Generation with Denoising Autoencoders - 2018

Research Area:  Machine Learning

Abstract:

Generating text from structured data is important for various tasks such as question answering and dialog systems. We show that in at least one domain, without any supervision and only based on unlabeled text, we are able to build a Natural Language Generation (NLG) system with higher performance than supervised approaches. In our approach, we interpret the structured data as a corrupt representation of the desired output and use a denoising auto-encoder to reconstruct the sentence. We show how to introduce noise into training examples that do not contain structured data, and that the resulting denoising auto-encoder generalizes to generate correct sentences when given structured data.

Author(s) Name:  Markus Freitag, Scott Roy

Journal name:  Computation and Language

Conferrence name:  

Publisher name:  arxiv

DOI:  arXiv:1804.07899

Volume Information: