Second language acquisition (SLA) research has extensively studied
cross-linguistic transfer, the influence of linguistic structure of a speaker's
native language [L1] on the successful acquisition of a foreign language [L2].
Effects of such transfer can be positive (facilitating acquisition) or negative
(impeding acquisition). We find that NLP literature has not given enough
attention to the phenomenon of negative transfer. To understand patterns of
both positive and negative transfer between L1 and L2, we model sequential
second language acquisition in LMs. Further, we build a Mutlilingual Age
Ordered CHILDES (MAO-CHILDES) -- a dataset consisting of 5 typologically
diverse languages, i.e., German, French, Polish, Indonesian, and Japanese -- to
understand the degree to which native Child-Directed Speech (CDS) [L1] can help
or conflict with English language acquisition [L2]. To examine the impact of
native CDS, we use the TILT-based cross lingual transfer learning approach
established by Papadimitriou and Jurafsky (2020) and find that, as in human
SLA, language family distance predicts more negative transfer. Additionally, we
find that conversational speech data shows greater facilitation for language
acquisition than scripted speech data. Our findings call for further research
using our novel Transformer-based SLA models and we would like to encourage it
by releasing our code, data, and models.

利用 Transformer-based SLA 模型研究母语语言 (L1) 对于英语作为第二语言 (L2) 学习的积极和消极转移，使用一个包括五种语言的多语言数据集，表明语言家族距离预测了更多的负面转移并且口头语言数据比脚本语言数据更有助于语言习得。

用 BERT 模型建模第二语言习得：SLABERT Talk Pretty One Day

SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with  BERT

This work examines the impact of cross-linguistic transfer on grammatical
errors in English as Second Language (ESL) texts. Using a computational
framework that formalizes the theory of Contrastive Analysis (CA), we
demonstrate that language specific error distributions in ESL writing can be
predicted from the typological properties of the native language and their
relation to the typology of English. Our typology driven model enables to
obtain accurate estimates of such distributions without access to any ESL data
for the target languages. Furthermore, we present a strategy for adjusting our
method to low-resource languages that lack typological documentation using a
bootstrapping approach which approximates native language typology from ESL
texts. Finally, we show that our framework is instrumental for linguistic
inquiry seeking to identify first language factors that contribute to a wide
range of difficulties in second language acquisition.

使用对比分析理论，研究跨语言转移对英语作为第二语言文字中的语法错误的影响，提出了一种基于语言类型学的模型，能够准确地预测 ESL 文本中的错误分布，并适用于缺乏语言类型学数据的语言。同时，该模型为探究第一语言因素对第二语言习得中难点的方法提供了工具。

基于语言类型学的对比分析：预测能力估算 ESL 语法错误分布

Contrastive Analysis with Predictive Power: Typology Driven Estimation  of Grammatical Error Distributions in ESL

Linguists and psychologists have long been studying cross-linguistic
transfer, the influence of native language properties on linguistic performance
in a foreign language. In this work we provide empirical evidence for this
process in the form of a strong correlation between language similarities
derived from structural features in English as Second Language (ESL) texts and
equivalent similarities obtained from the typological features of the native
languages. We leverage this finding to recover native language typological
similarity structure directly from ESL text, and perform prediction of
typological features in an unsupervised fashion with respect to the target
languages. Our method achieves 72.2% accuracy on the typology prediction task,
a result that is highly competitive with equivalent methods that rely on
typological resources.

本研究通过英语作为第二语言文本中的结构特征和母语语言的类型学特征之间的相似性，提供了支持语言相似性跨语言转移的实证证据，并利用这一发现，在不依赖类型学资源的情况下，直接从 ESL 文本中恢复母语类型学相似性结构，并以无监督的方式执行对目标语言的类型学特征预测。我们的方法在类型学预测任务上取得了 72.2％的准确度，具有与依赖类型学资源的等效方法相当的竞争力。