With the success of neural language models (LMs), their language acquisition has gained much attention. This work sheds light on the second language (L2) acquisition of LMs, while previous work has typically explored their first language (L1) acquisition. Specifically, we trained bilingual LMs with a scenario similar to human L2 acquisition and analyzed their cross-lingual transfer from linguistic perspectives. Our exploratory experiments demonstrated that the L1 pretraining accelerated their linguistic generalization in L2, and language transfer configurations (e.g., the L1 choice, and presence of parallel texts) substantially affected their generalizations. These clarify their (non-)human-like L2 acquisition in particular aspects.

翻译：随着神经语言模型（LMs）的成功，其语言习得能力受到广泛关注。本研究聚焦于语言模型的第二语言（L2）习得，而以往研究通常探索其第一语言（L1）习得。具体而言，我们通过模拟人类L2习得场景训练双语语言模型，并从语言学视角分析其跨语言迁移能力。探索性实验表明，L1预训练加速了模型在L2中的语言泛化能力，而语言迁移配置（如L1的选择、平行文本的存在与否）显著影响其泛化表现。这些发现揭示了模型在特定方面具有（非）类人L2习得特征。