Contact between languages has the potential to transmit vocabulary and other
language features; however, this does not always happen. Here, an iterated
learning model is used to examine, in a simple way, the resistance of languages
to change during language contact. Iterated learning models are agent-based
models of language change, they demonstrate that languages that are expressive
and compositional arise spontaneously as a consequence of a language
transmission bottleneck. A recently introduced type of iterated learning model,
the Semi-Supervised ILM is used to simulate language contact. These simulations
do not include many of the complex factors involved in language contact and do
not model a population of speakers; nonetheless the model demonstrates that the
dynamics which lead languages in the model to spontaneously become expressive
and compositional, also cause a language to maintain its core traits even after
mixing with another language.

通过迭代学习模型研究语言接触过程中的变化抵抗性，表明即使与其他语言混合后，语言仍然保持其核心特征。

使用迭代学习模型建模语言接触情况

Modeling language contact with the Iterated Learning Model

Naively assuming English as a source language may hinder cross-lingual
transfer for many languages by failing to consider the importance of language
contact. Some languages are more well-connected than others, and target
languages can benefit from transferring from closely related languages; for
many languages, the set of closely related languages does not include English.
In this work, we study the impact of source language for cross-lingual
transfer, demonstrating the importance of selecting source languages that have
high contact with the target language. We also construct a novel benchmark
dataset for close contact Chinese-Japanese-Korean-Vietnamese (CJKV) languages
to further encourage in-depth studies of language contact. To comprehensively
capture contact between these languages, we propose to integrate Romanized
transcription beyond textual scripts via Contrastive Learning objectives,
leading to enhanced cross-lingual representations and effective zero-shot
cross-lingual transfer.

通过考虑语言接触的重要性，本研究研究了源语言对跨语言转移的影响，并提出了一种整合罗马化转录的方法来增强跨语言表示和有效的零样本跨语言转移，进而鼓励对接触语言的深入研究。

CORI: 汉字罗马化整合的 CJKV 基准 -- 跨语言文字脚本转换的一步

CORI: CJKV Benchmark with Romanization Integration -- A step towards  Cross-lingual Transfer Beyond Textual Scripts

Language contact is a pervasive phenomenon reflected in the borrowing of
words from donor to recipient languages. Most computational approaches to
borrowing detection treat all languages under study as equally important, even
though dominant languages have a stronger impact on heritage languages than
vice versa. We test new methods for lexical borrowing detection in contact
situations where dominant languages play an important role, applying two
classical sequence comparison methods and one machine learning method to a
sample of seven Latin American languages which have all borrowed extensively
from Spanish. All methods perform well, with the supervised machine learning
system outperforming the classical systems. A review of detection errors shows
that borrowing detection could be substantially improved by taking into account
donor words with divergent meanings from recipient words.

研究探讨了语言接触中主导语言对被介入语言的影响，通过使用三种方法（其中包括两种传统的序列比较方法和一种机器学习方法）在拉丁美洲七种常常和西班牙语有大量交流和借词的语言中测试新型的词汇借用检测方法，并通过检测中的误差反映出通过考虑来自受体语言不同意义的捐赠词汇可以大大提高借贷检测的精度。