In this paper, we introduce a benchmark for evaluating the overall quality of emergent languages using data-driven methods. Specifically, we interpret the notion of the "quality" of an emergent language as its similarity to human language within a deep learning framework. We measure this by using the emergent language as pretraining data for a downstream NLP tasks in human language -- the better the downstream performance, the better the emergent language. We implement this benchmark as an easy-to-use Python package that only requires a text file of utterances from the emergent language to be evaluated. Finally, we empirically test the benchmark's validity using human, synthetic, and emergent language baselines.
@article{arxiv.2407.03456,
title = {XferBench: a Data-Driven Benchmark for Emergent Language},
author = {Brendon Boldt and David Mortensen},
journal= {arXiv preprint arXiv:2407.03456},
year = {2024}
}