The Unnatural Language ToolKit (ULTK)

Four examples of many recent results showing that natural languages are optimized for efficient communication.

Introduction

ULTK is a software library that aims to support efficient communication analyses of natural language. This is a line of research that aims to explain why natural languages have the structure that they do in terms competing pressures to minimize cognitive complexity and maximize communicative accuracy.

Key features:

Primitives for constructing semantic spaces, expressions, and languages
Tools for measuring informativity of languages, communicative success of RSA speakers and listeners
Language population sampling and optimization w.r.t Pareto fronts
Rate-Distortion and Information Bottleneck style analyses

ULTK is a long term project and it is currently in its early stages. It is intended to help lower the barrier to entry for certain research in computational semantics, and to unify methodologies. If you find something confusing, please open an issue. If you have a phenomena of interest in linguistic semantics that you want to run an efficient communication analysis on, please contact the contributors.

Read the documentation.

Installing ULTK

First, set up a virtual environment (e.g. via miniconda, conda create -n ultk python=3.11, and conda activate ultk).

Download or clone this repository and navigate to the root folder.
Install ULTK (We recommend doing this inside a virtual environment)

pip install -e .

Getting started

Check out the examples, starting with a basic signaling game. The examples folder also contains a simiple efficient communication analysis of indefinites.
To see more scaled up usage examples, visit the codebase for an efficient communication analysis of modals or sim-max games.
For an introduction to efficient communication research, here is a survey paper of the field.
For an introduction to the RSA framework, see this online textbook.

Modules

There are two modules. The first is ultk.effcomm, which includes methods for measuring informativity of languages and/or communicative success of Rational Speech Act agents, and for language population sampling and optimization w.r.t Pareto fronts.

The second module is ultk.language, which contains primitives for constructing semantic spaces, expressions, and languages. It also has a grammar module which can be used for building expressions in a Language of Thought and measuring complexity in terms of minimum description length, as well as for natural language syntax.

The source code is available on github here.

Testing

Unit tests are written in pytest and executed via running pytest in the src/tests folder.

References

Figures:

> Kemp, C. & Regier, T. (2012) Kinship Categories Across Languages Reflect General Communicative Principles. Science. https://www.science.org/doi/10.1126/science.1218811 > Zaslavsky, N., Kemp, C., Regier, T., & Tishby, N. (2018). Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences, 115(31), 7937–7942. https://doi.org/10.1073/pnas.1800521115 > Denić, M., Steinert-Threlkeld, S., & Szymanik, J. (2022). Indefinite Pronouns Optimize the Simplicity/Informativeness Trade-Off. Cognitive Science, 46(5), e13142. https://doi.org/10.1111/cogs.13142 > Steinert-Threlkeld, S. (2021). Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals. Entropy, 23(10), Article 10. https://doi.org/10.3390/e23101335

Links:

> Imel, N. (2023). The evolution of efficient compression in signaling games. PsyArXiv. https://doi.org/10.31234/osf.io/b62de > Imel, N., & Steinert-Threlkeld, S. (2022). Modal semantic universals optimize the simplicity/informativeness trade-off. Semantics and Linguistic Theory, 1(0), Article 0. https://doi.org/10.3765/salt.v1i0.5346 > Kemp, C., Xu, Y., & Regier, T. (2018). Semantic Typology and Efficient Communication. Annual Review of Linguistics, 4(1), 109–128. https://doi.org/10.1146/annurev-linguistics-011817-045406

CLMBRs / ultk

readme