mlsanityData to 14 Oct 2026

Natural Language Generation Using Link Grammar for General Conversational Intelligence

arXiv 2105.00830 ↗First posted 19 Apr 2021cs.CL cs.AIcited by 2

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Abstract (arXiv)

Many current artificial general intelligence (AGI) and natural language processing (NLP) architectures do not possess general conversational intelligence--that is, they either do not deal with language or are unable to convey knowledge in a form similar to the human language without manual, labor-intensive methods such as template-based customization. In this paper, we propose a new technique to automatically generate grammatically valid sentences using the Link Grammar database. This natural language generation method far outperforms current state-of-the-art baselines and may serve as the final component in a proto-AGI question answering pipeline that understandably handles natural language material.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 19 Apr 2021.

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9 of 22 references.

References · 9

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  8. 2019A Comparison of Semantic Similarity Methods for Maximum Human Interpretabilitycited by 9
  9. 2020Controlled Language and Baby Turing Test for General Conversational Intelligencecited by 1

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Citing papers per year: 2022: 2.

2 papers.

  1. 2022Unsupervised Tokenization Learningcited by 2
  2. 2022On Unsupervised Training of Link Grammar Based Language Modelscited by 0