# The NLP Engine: A Universal Turing Machine for NLP

arXiv: https://arxiv.org/abs/1503.00168 · HTML: https://mlsanity.com/paper/1503.00168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2015.

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

- [AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents](https://mlsanity.com/paper/2405.06907/) (2024)
- [Leveraging Natural Supervision for Language Representation Learning and Generation](https://mlsanity.com/paper/2207.10617/) (2022)

## Abstract (arXiv)

It is commonly accepted that machine translation is a more complex task than part of speech tagging. But how much more complex? In this paper we make an attempt to develop a general framework and methodology for computing the informational and/or processing complexity of NLP applications and tasks. We define a universal framework akin to a Turning Machine that attempts to fit (most) NLP tasks into one paradigm. We calculate the complexities of various NLP tasks using measures of Shannon Entropy, and compare `simple' ones such as part of speech tagging to `complex' ones such as machine translation. This paper provides a first, though far from perfect, attempt to quantify NLP tasks under a uniform paradigm. We point out current deficiencies and suggest some avenues for fruitful research.