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Related Experiment Video

Updated: Nov 17, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant.

Anda Stoica1, Tibor Kadar1, Camelia Lemnaru1

  • 1Department of Computer Science, Technical University of Cluj-Napoca, 26-28 G. Baritiu, 400027 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|February 12, 2021
PubMed
Summary

This study introduces a capsule neural network for Romanian Natural Language Understanding (NLU) in virtual home assistants. The model significantly improves intent detection compared to existing tools, despite language variability challenges.

Keywords:
NLURomanian home assistantcapsule neural networksintent detectionslot filling

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Growing popularity of virtual home assistants necessitates broader language support beyond English.
  • Less common languages like Romanian are currently underrepresented in commercial virtual home assistant systems.
  • Existing Natural Language Understanding (NLU) tools may not adequately address the complexities of less-resourced languages.

Purpose of the Study:

  • To explore the application of Natural Language Understanding (NLU) for a Romanian virtual home assistant.
  • To propose and evaluate a customized capsule neural network architecture for joint intent detection and slot filling.
  • To assess the model's performance on Romanian utterances with varying complexity levels.

Main Methods:

  • Development of a specialized capsule neural network architecture tailored for Romanian NLU tasks.
  • Joint implementation of intent detection and slot filling within the proposed capsule network model.
  • Comparative analysis against established NLU tools, specifically Rasa NLU, using Romanian language data.

Main Results:

  • The capsule network architecture demonstrated significant improvements in intent detection accuracy compared to Rasa NLU.
  • Systematic error patterns were identified through detailed error analysis, highlighting specific linguistic challenges.
  • Language variability in expressing a single intent emerged as the primary obstacle for the model's performance.

Conclusions:

  • Capsule neural networks offer a promising approach for enhancing NLU capabilities in under-resourced languages for virtual assistants.
  • The proposed model provides a robust solution for joint intent detection and slot filling in Romanian.
  • Addressing linguistic variability is crucial for future advancements in NLU for diverse language applications.