@inproceedings{boldt-mortensen-2024-xferbench,
    title = "{X}fer{B}ench: a Data-Driven Benchmark for Emergent Language",
    author = "Boldt, Brendon  and
      Mortensen, David",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.naacl-long.82/",
    doi = "10.18653/v1/2024.naacl-long.82",
    pages = "1475--1489",
    abstract = "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."
}