@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T XferBench: a Data-Driven Benchmark for Emergent Language
%A Boldt, Brendon
%A Mortensen, David
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F boldt-mortensen-2024-xferbench
%X 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.
%U https://aclanthology.org/2024.naacl-long.82
%P 1475-1489
Markdown (Informal)
[XferBench: a Data-Driven Benchmark for Emergent Language](https://aclanthology.org/2024.naacl-long.82) (Boldt & Mortensen, NAACL 2024)
ACL
- Brendon Boldt and David Mortensen. 2024. XferBench: a Data-Driven Benchmark for Emergent Language. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1475–1489, Mexico City, Mexico. Association for Computational Linguistics.