用音素流预训练语言模型，探索语音层面的语义理解新路径
From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes
- 将文本数据转换为连续音素流，实现音素级语言模型预训练
- 在传统任务上性能略有下降，但提升语音相关任务的可解释性
- 适合研究语音认知、语音生成与音素建模的学者与工程师
语言模型通常以文本形式进行大规模预训练，但将数据表示为音素流也具有独特优势，如深入理解语音习得机制或提升语音相关任务表现。挑战在于缺乏对应的音素评估基准。为此，我们开发了一套将文本数据转化为连续音素流的管道，应用于婴儿语言模型挑战赛中的1亿词预训练数据集以及标准语言与语法测试集，实现了基于音素输入的模型预训练与评估。结果表明，音素训练在传统语言理解任务上性能略有下降，但在语音分析和实际应用中展现出显著价值。
原文摘要 · Abstract (English)
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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