Maltese is often described as having a hybrid morphological system resulting
from extensive contact between Semitic and Romance language varieties. Such a
designation reflects an etymological divide as much as it does a larger
tradition in the literature to consider concatenative and non-concatenative
morphological patterns as distinct in the language architecture. Using a
combination of computational modeling and information theoretic methods, we
quantify the extent to which the phonology and etymology of a Maltese singular
noun may predict the morphological process (affixal vs. templatic) as well as
the specific plural allomorph (affix or template) relating a singular noun to
its associated plural form(s) in the lexicon. The results indicate phonological
pressures shape the organization of the Maltese lexicon with predictive power
that extends beyond that of a word's etymology, in line with analogical
theories of language change in contact.

本文使用计算模型和信息论方法定量分析了马耳他语单数名词的语音和词源学对其形态学处理 (前缀 vs. 模板) 以及单一名词如何变化为复数形式的特定词源形式 (前缀或模板) 的影响，结果表明，类比语言变化理论的核心在于音系的压力塑造了马耳他语的词汇结构，这种压力具有超越词源学的预测能力。

接触中的类比：塑造马耳他语复数变形

Analogy in Contact: Modeling Maltese Plural Inflection

Multilingual language models such as mBERT have seen impressive cross-lingual
transfer to a variety of languages, but many languages remain excluded from
these models. In this paper, we analyse the effect of pre-training with
monolingual data for a low-resource language that is not included in mBERT --
Maltese -- with a range of pre-training set ups. We conduct evaluations with
the newly pre-trained models on three morphosyntactic tasks -- dependency
parsing, part-of-speech tagging, and named-entity recognition -- and one
semantic classification task -- sentiment analysis. We also present a newly
created corpus for Maltese, and determine the effect that the pre-training data
size and domain have on the downstream performance. Our results show that using
a mixture of pre-training domains is often superior to using Wikipedia text
only. We also find that a fraction of this corpus is enough to make significant
leaps in performance over Wikipedia-trained models. We pre-train and compare
two models on the new corpus: a monolingual BERT model trained from scratch
(BERTu), and a further pre-trained multilingual BERT (mBERTu). The models
achieve state-of-the-art performance on these tasks, despite the new corpus
being considerably smaller than typically used corpora for high-resourced
languages. On average, BERTu outperforms or performs competitively with mBERTu,
and the largest gains are observed for higher-level tasks.

本文分析了使用单语数据进行预训练对于 mBERT 中未包含的低资源语种（如马耳他语）的效果，并研究了新的马耳他语语料库的大小和域对下游任务性能的影响。研究表明，使用混合预训练域往往优于仅使用维基百科文本，并且只有一小部分的马耳他语语料库就足以在任务性能上取得显著提高。此外，本文还预训练并比较了两个模型：从头开始训练的单语 BERT 模型（BERTu）和进一步预训练的多语言 BERT 模型（mBERTu），这两个模型都在各种下游任务上取得了最先进的性能。