  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.
