# Natural Language Generation Using Link Grammar for General Conversational Intelligence

arXiv: https://arxiv.org/abs/2105.00830 · HTML: https://mlsanity.com/paper/2105.00830/

## 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.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://mlsanity.com/paper/1508.01745/) (2015)
- [Unsupervised Natural Language Generation with Denoising Autoencoders](https://mlsanity.com/paper/1804.07899/) (2018)
- [A Comparison of Semantic Similarity Methods for Maximum Human Interpretability](https://mlsanity.com/paper/1910.09129/) (2019)
- [Controlled Language and Baby Turing Test for General Conversational Intelligence](https://mlsanity.com/paper/2005.09280/) (2020)

## Built on by (most cited first, 2 of 2)

- [Unsupervised Tokenization Learning](https://mlsanity.com/paper/2205.11443/) (2022)
- [On Unsupervised Training of Link Grammar Based Language Models](https://mlsanity.com/paper/2208.13021/) (2022)

## 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.