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Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication.

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  • 1Center for Spoken Language Understanding, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd., Portland, Oregon, USA.

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Summary
This summary is machine-generated.

This study introduces a novel method for creating training data to improve predictive typing in icon-based Augmentative and Alternative Communication (AAC) systems. The research explores new modeling strategies to enhance AAC device functionality.

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Area of Science:

  • Augmentative and Alternative Communication (AAC)
  • Natural Language Processing (NLP)

Background:

  • Icon-based systems are prevalent in AAC, yet often lack advanced predictive typing.
  • Challenges in training icon-based language models hinder their predictive capabilities compared to text-based systems.

Purpose of the Study:

  • To propose a method for synthesizing training data for icon-based language models.
  • To explore different modeling strategies for enhancing predictive typing in icon-based AAC.

Main Methods:

  • Development of a novel data synthesis technique for icon-based language models.
  • Evaluation of two distinct modeling approaches for predictive typing.

Main Results:

  • The proposed data synthesis method facilitates the training of more effective icon-based language models.
  • Exploration of modeling strategies identified potential improvements in predictive typing accuracy and efficiency.

Conclusions:

  • The developed data synthesis approach addresses a key limitation in icon-based AAC.
  • Further research into modeling strategies can significantly advance predictive typing for AAC users.