Theoretical work in morphological typology offers the possibility of
measuring morphological diversity on a continuous scale. However, literature in
Natural Language Processing (NLP) typically labels a whole language with a
strict type of morphology, e.g. fusional or agglutinative. In this work, we
propose to reduce the rigidity of such claims, by quantifying morphological
typology at the word and segment level. We consider Payne (2017)'s approach to
classify morphology using two indices: synthesis (e.g. analytic to
polysynthetic) and fusion (agglutinative to fusional). For computing synthesis,
we test unsupervised and supervised morphological segmentation methods for
English, German and Turkish, whereas for fusion, we propose a semi-automatic
method using Spanish as a case study. Then, we analyse the relationship between
machine translation quality and the degree of synthesis and fusion at word
(nouns and verbs for English-Turkish, and verbs in English-Spanish) and segment
level (previous language pairs plus English-German in both directions). We
complement the word-level analysis with human evaluation, and overall, we
observe a consistent impact of both indexes on machine translation quality.

通过量化语言单词和片段级别的形态学来降低整体语言严格形态类型的刻板印象，测试了无监督和监督的形态分割方法，并提出了一种半自动的融合方法，探讨了机器翻译质量与合成和融合程度之间的关系，并在英语、德语、土耳其和西班牙语上进行了实验。

量化合成和融合对机器翻译的影响

Quantifying Synthesis and Fusion and their Impact on Machine Translation

As NLP tools become ubiquitous in today's technological landscape, they are
increasingly applied to languages with a variety of typological structures.
However, NLP research does not focus primarily on typological differences in
its analysis of state-of-the-art language models. As a result, NLP tools
perform unequally across languages with different syntactic and morphological
structures. Through a detailed discussion of word order typology, morphological
typology, and comparative linguistics, we identify which variables most affect
language modeling efficacy; in addition, we calculate word order and
morphological similarity indices to aid our empirical study. We then use this
background to support our analysis of an experiment we conduct using
multi-class text classification on eight languages and eight models.

研究探讨了 NLP 工具在不同语言的应用情况以及针对语言结构差异进行的分析，指出哪些变量最影响语言建模的效果，通过计算词序和形态相似性指数等方法进行实证研究，最后通过多分类文本分类实验验证了研究结论。

评估基于 Transformer 的多语言文本分类

Evaluating Transformer-Based Multilingual Text Classification

Words can be represented by composing the representations of subword units
such as word segments, characters, and/or character n-grams. While such
representations are effective and may capture the morphological regularities of
words, they have not been systematically compared, and it is not understood how
they interact with different morphological typologies. On a language modeling
task, we present experiments that systematically vary (1) the basic unit of
representation, (2) the composition of these representations, and (3) the
morphological typology of the language modeled. Our results extend previous
findings that character representations are effective across typologies, and we
find that a previously unstudied combination of character trigram
representations composed with bi-LSTMs outperforms most others. But we also
find room for improvement: none of the character-level models match the
predictive accuracy of a model with access to true morphological analyses, even
when learned from an order of magnitude more data.

通过系统地比较不同的基本表示单元、表示单元的组合方式和建模的语言的形态学类型，我们发现用 bi-LSTM 组合字符三元组表示法在大多数情况下优于其他方法，但是没有任何一种基于字符级别的模型能够与直接使用真实的形态学分析相比拟，即使学习数据增加一个数量级。