We present methods for calculating a measure of phonotactic complexity---bits
per phoneme---that permits a straightforward cross-linguistic comparison. When
given a word, represented as a sequence of phonemic segments such as symbols in
the international phonetic alphabet, and a statistical model trained on a
sample of word types from the language, we can approximately measure bits per
phoneme using the negative log-probability of that word under the model. This
simple measure allows us to compare the entropy across languages, giving
insight into how complex a language's phonotactics are. Using a collection of
1016 basic concept words across 106 languages, we demonstrate a very strong
negative correlation of -0.74 between bits per phoneme and the average length
of words.

本研究介绍了一种计算语音统计复杂度的方法，在给定来自不同语言的序列化单词和样本统计模型的基础上，通过负对数概率的近似值来比较语言的熵，发现 bits per phoneme 与平均单词长度之间呈现非常强的负相关关系

音位结构复杂性及其权衡

Phonotactic Complexity and its Trade-offs

For general modeling methods applied to diverse languages, a natural question
is: how well should we expect our models to work on languages with differing
typological profiles? In this work, we develop an evaluation framework for fair
cross-linguistic comparison of language models, using translated text so that
all models are asked to predict approximately the same information. We then
conduct a study on 21 languages, demonstrating that in some languages, the
textual expression of the information is harder to predict with both $n$-gram
and LSTM language models. We show complex inflectional morphology to be a cause
of performance differences among languages.

本文使用翻译文本开发了一种公平的跨语言语言模型比较方法，在 21 种语言中展示了复杂的屈折形态是导致不同语言性能差异的原因。

所有语言的语言模型难度是否相同？

Are All Languages Equally Hard to Language-Model?

Automatic image description systems are commonly trained and evaluated on
large image description datasets. Recently, researchers have started to collect
such datasets for languages other than English. An unexplored question is how
different these datasets are from English and, if there are any differences,
what causes them to differ. This paper provides a cross-linguistic comparison
of Dutch, English, and German image descriptions. We find that these
descriptions are similar in many respects, but the familiarity of crowd workers
with the subjects of the images has a noticeable influence on description
specificity.

通过对英语、荷兰语和德语图像描述的跨语言比较，发现众包工作者对图像的熟悉程度对图像描述的具体性有明显影响。