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.- Anthology ID:
- 2024.naacl-long.82
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1475–1489
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.82
- DOI:
- 10.18653/v1/2024.naacl-long.82
- Cite (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.
- Cite (Informal):
- XferBench: a Data-Driven Benchmark for Emergent Language (Boldt & Mortensen, NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.82.pdf