Modern contextual lemmatizers often rely on automatically induced Shortest
Edit Scripts (SES), namely, the number of edit operations to transform a word
form into its lemma. In fact, different methods of computing SES have been
proposed as an integral component in the architecture of several
state-of-the-art contextual lemmatizers currently available. However, previous
work has not investigated the direct impact of SES in the final lemmatization
performance. In this paper we address this issue by focusing on lemmatization
as a token classification task where the only input that the model receives is
the word-label pairs in context, where the labels correspond to previously
induced SES. Thus, by modifying in our lemmatization system only the SES labels
that the model needs to learn, we may then objectively conclude which SES
representation produces the best lemmatization results. We experiment with
seven languages of different morphological complexity, namely, English,
Spanish, Basque, Russian, Czech, Turkish and Polish, using multilingual and
language-specific pre-trained masked language encoder-only models as a backbone
to build our lemmatizers. Comprehensive experimental results, both in- and
out-of-domain, indicate that computing the casing and edit operations
separately is beneficial overall, but much more clearly for languages with
high-inflected morphology. Notably, multilingual pre-trained language models
consistently outperform their language-specific counterparts in every
evaluation setting.

通过研究多种语言的上下文词形还原系统，本文发现计算大小写和编辑操作可以改善该系统的性能，尤其对于具有高派生形态的语言。值得注意的是，多语言预训练语言模型在每个评估环节中始终优于特定语言的模型。

评估用于上下文词形还原的最短编辑脚本方法

Evaluating Shortest Edit Script Methods for Contextual Lemmatization

Lemmatization is a Natural Language Processing (NLP) task which consists of
producing, from a given inflected word, its canonical form or lemma.
Lemmatization is one of the basic tasks that facilitate downstream NLP
applications, and is of particular importance for high-inflected languages.
Given that the process to obtain a lemma from an inflected word can be
explained by looking at its morphosyntactic category, including fine-grained
morphosyntactic information to train contextual lemmatizers has become common
practice, without analyzing whether that is the optimum in terms of downstream
performance. Thus, in this paper we empirically investigate the role of
morphological information to develop contextual lemmatizers in six languages
within a varied spectrum of morphological complexity: Basque, Turkish, Russian,
Czech, Spanish and English. Furthermore, and unlike the vast majority of
previous work, we also evaluate lemmatizers in out-of-domain settings, which
constitutes, after all, their most common application use. The results of our
study are rather surprising: (i) providing lemmatizers with fine-grained
morphological features during training is not that beneficial, not even for
agglutinative languages; (ii) in fact, modern contextual word representations
seem to implicitly encode enough morphological information to obtain good
contextual lemmatizers without seeing any explicit morphological signal; (iii)
the best lemmatizers out-of-domain are those using simple UPOS tags or those
trained without morphology; (iv) current evaluation practices for lemmatization
are not adequate to clearly discriminate between models.

本文通过实证研究，考察了在六种不同的语言上，使用不同的形态学特征开发上下文词形还原器对下游表现的影响，并发现：（i）为词形还原器提供细粒度的形态学特征在训练时并不那么有益，即使对于词汇连接语言；（ii）实际上，现代上下文词表示似乎隐式地编码了足够的形态信息，以获得无需查看任何明确形态信号的良好上下文词形还原器；（iii）最佳的领域外词形还原器是使用简单的 UPOS 标签或者没有训练形态学的模型；（iv）目前的词形还原评估实践并不足以清晰地区分模型之间的差异。