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Found 2 papers, 1 papers with code
Evaluating Shortest Edit Script Methods for Contextual Lemmatization
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.
On the Role of Morphological Information for Contextual Lemmatization
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 considering whether that is the optimum in terms of downstream performance.
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