XferBench: a Data-Driven Benchmark for Emergent Language
Autor: | Boldt, Brendon, Mortensen, David |
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Rok vydání: | 2024 |
Předmět: | |
Zdroj: | Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 1475-1489 |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: 15 pages, 5 figures |
Databáze: | arXiv |
Externí odkaz: |