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arXiv · 2410.22906

From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes

Zébulon Goriely·Richard Diehl Martinez·Andrew Caines·Lisa Beinborn·Paula Buttery
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Abstract

Language models are typically trained on large corpora of text in their default orthographic form. However, this is not the only option; representing data as streams of phonemes can offer unique advantages, from deeper insights into phonological language acquisition to improved performance on sound-based tasks. The challenge lies in evaluating the impact of phoneme-based training, as most benchmarks are also orthographic. To address this, we develop a pipeline to convert text datasets into a continuous stream of phonemes. We apply this pipeline to the 100-million-word pre-training dataset from the BabyLM challenge, as well as to standard language and grammatical benchmarks, enabling us to pre-train and evaluate a model using phonemic input representations. Our results show that while phoneme-based training slightly reduces performance on traditional language understanding tasks, it offers valuable analytical and practical benefits.

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Zébulon Goriely, Richard Diehl Martinez, Andrew Caines, Lisa Beinborn, Paula Buttery. 2024-10-30. From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes. https://arxiv.org/abs/2410.22906

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