在双语场景中学习儿童引导的口语：一项法语-英语案例研究
Learning from Child-Directed Speech in Two-Language Scenarios: A French-English Case Study
AI总结：
本文研究了在严格数据匹配条件下，扩展BabyBERTa至英法双语场景的紧凑语言模型，对比儿童口语与多领域语料对语法和语义任务的影响，发现儿童口语在单语任务中表现更优，双语预训练对文本蕴含任务有显著提升。
摘要：
关于具有发展合理性的语言模型的研究大多集中在英语上，留下多语言环境中的问题。我们通过将BabyBERTa扩展到英语-法语场景，在严格的数据匹配条件下，系统研究了紧凑语言模型，涵盖单语、双语和跨语言设置。我们的设计对比了两种类型的训练语料：（i）儿童引导的口语（约250万词），遵循BabyBERTa及相关工作；（ii）多领域语料（约1000万词），将BabyLM框架扩展到法语。为了实现公平评估，我们还引入了新的资源，包括QAMR和QASRL的法语版本，以及英语和法语的多领域语料。我们评估了模型在语法和语义任务上的表现，并将其与仅训练于维基百科数据的模型进行比较。结果揭示了上下文依赖效应：训练于维基百科数据对语义任务有持续好处，而儿童口语在单语设置中改善了语法判断。双语预训练在文本蕴含任务中产生显著提升，尤其是对法语表现特别突出。重要的是，相似的模式在BabyBERTa、RoBERTa和LTG-BERT中出现，表明在不同架构中存在一致的趋势。
英文摘要：
Research on developmentally plausible language models has largely focused on English, leaving open questions about multilingual settings. We present a systematic study of compact language models by extending BabyBERTa to English-French scenarios under strictly size-matched data conditions, covering monolingual, bilingual, and cross-lingual settings. Our design contrasts two types of training corpora: (i) child-directed speech (about 2.5M tokens), following BabyBERTa and related work, and (ii) multi-domain corpora (about 10M tokens), extending the BabyLM framework to French. To enable fair evaluation, we also introduce new resources, including French versions of QAMR and QASRL, as well as English and French multi-domain corpora. We evaluate the models on both syntactic and semantic tasks and compare them with models trained on Wikipedia-only data. The results reveal context-dependent effects: training on Wikipedia consistently benefits semantic tasks, whereas child-directed speech improves grammatical judgments in monolingual settings. Bilingual pretraining yields notable gains for textual entailment, with particularly strong improvements for French. Importantly, similar patterns emerge across BabyBERTa, RoBERTa, and LTG-BERT, suggesting consistent trends across architectures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
