  We propose a novel hybrid approach to lemmatization that enhances the seq2seq
neural model with additional lemmas extracted from an external lexicon or a
rule-based system. During training, the enhanced lemmatizer learns both to
generate lemmas via a sequential decoder and copy the lemma characters from the
external candidates supplied during run-time. Our lemmatizer enhanced with
candidates extracted from the Apertium morphological analyzer achieves
statistically significant improvements compared to baseline models not
utilizing additional lemma information, achieves an average accuracy of 97.25%
on a set of 23 UD languages, which is 0.55% higher than obtained with the
Stanford Stanza model on the same set of languages. We also compare with other
methods of integrating external data into lemmatization and show that our
enhanced system performs considerably better than a simple lexicon extension
method based on the Stanza system, and it achieves complementary improvements
w.r.t. the data augmentation method.
