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2020 | OriginalPaper | Chapter

Attentive Natural Language Generation from Abstract Meaning Representation

Authors : Radha Senthilkumar, S. Afrish Khan

Published in: New Trends in Computational Vision and Bio-inspired Computing

Publisher: Springer International Publishing

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Abstract

Natural Language Generation takes a key role in presenting data as text or speech. The translation into a natural language from semantic representation is similar to Neural Machine Translation. We use a similar methodology known as Seq2Seq modelling for generating natural language. The usage of common semantic representation such as Abstract Meaning Representation allows adding naturalness to the generated sentences while being domain neutral. Recurrent Neural Network based autoencoder learns a hidden representation from semantic input which is then used to generate natural language. Long-Short Term Memory while in theory being capable of learning long-term dependencies fails to capture the correct information required for generation. We introduce attention mechanism as a resolution to improve capturing contextually important information. The resulting model has a significantly improved accuracy.

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Metadata
Title
Attentive Natural Language Generation from Abstract Meaning Representation
Authors
Radha Senthilkumar
S. Afrish Khan
Copyright Year
2020
Publisher
Springer International Publishing
Book
New Trends in Computational Vision and Bio-inspired Computing

Print ISBN: 978-3-030-41861-8

Electronic ISBN: 978-3-030-41862-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41862-5_169

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