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Attentive Natural Language Generation from Abstract Meaning Representation

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

Author

Listed:
  • Radha Senthilkumar

    (MIT, Anna University, Information Technology)

  • S. Afrish Khan

    (MIT, Anna University, Information Technology)

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.

Suggested Citation

  • Radha Senthilkumar & S. Afrish Khan, 2020. "Attentive Natural Language Generation from Abstract Meaning Representation," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1649-1657, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_169
    DOI: 10.1007/978-3-030-41862-5_169
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