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Updated: Jun 6, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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NLAS-multi: A multilingual corpus of automatically generated Natural Language Argumentation Schemes.

Ramon Ruiz-Dolz1, Joaquin Taverner2, John Lawrence1

  • 1Centre for Argument Technology, University of Dundee, Dundee DD1 4HN, United Kingdom.

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This study introduces a new method for automatically generating natural language arguments across various topics and languages. It also presents the largest corpus of Natural Language Argumentation Schemes to date, addressing key limitations in argument analysis.

Keywords:
Argument miningComputational argumentationNatural language generation

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Argument mining and generation face challenges with data annotation complexity.
  • Limited corpus sizes and multilingual/domain constraints hinder progress in argument analysis.

Purpose of the Study:

  • To develop an effective methodology for automatic natural language argument generation.
  • To create the largest publicly available corpus of Natural Language Argumentation Schemes.

Main Methods:

  • Developed a novel methodology for automated argument generation.
  • Compiled and released a comprehensive corpus of argumentation schemes.

Main Results:

  • Successfully generated natural language arguments across diverse topics and languages.
  • Established the largest public corpus for Natural Language Argumentation Schemes.

Conclusions:

  • The proposed methodology effectively addresses limitations in argument generation.
  • The new corpus significantly advances research in natural language argument analysis and mining.