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Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks.

Florin Macicasan1, Alexandru Frasie1, Nicoleta-Teodora Vezan1

  • 1Knowledge Engineering Research Group, Technical University of Cluj-Napoca, Cluj-Napoca 400027, Romania.

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Summary

This study enhances meaning representation parsing by integrating advanced dependency parsing techniques into a two-stage AMR parser. The improvements focus on handling out-of-vocabulary words and optimizing relation identification for better text meaning extraction.

Keywords:
LSTMNatural language processingabstract meaning representationconcept identificationdynamic neural networksrelation identification

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Meaning Representation Parsing (MRP) aims to extract semantic meaning from text by constructing Directed, Acyclic Graphs (DAGs).
  • Existing MRP systems often face challenges with out-of-vocabulary words and optimizing complex parsing pipelines.

Purpose of the Study:

  • To enhance a two-stage Abstract Meaning Representation (AMR) parser by incorporating state-of-the-art dependency parsing techniques.
  • To improve the handling of out-of-vocabulary words and the performance of relation identification within the AMR parsing pipeline.

Main Methods:

  • Utilized Pointer-Generator Networks with improved word and character-level embeddings for concept identification, addressing out-of-vocabulary words.
  • Jointly trained Heads Selection and Arcs Labeling components within the Relation Identification module to boost performance.
  • Explored dynamic computation graph construction as an alternative to static approaches for enabling end-to-end training.

Main Results:

  • Achieved improved concept identification for out-of-vocabulary words through Pointer-Generator Networks and enhanced embeddings.
  • Enhanced the performance of the Relation Identification module by jointly optimizing its sub-components.
  • Demonstrated the potential of dynamic construction for enabling end-to-end training in complex parsing pipelines.

Conclusions:

  • The integration of advanced dependency parsing techniques significantly improves AMR parsing performance.
  • Dynamic computation graph construction offers a promising direction for achieving end-to-end training in MRP.
  • The proposed enhancements contribute to more robust and accurate meaning extraction from text.