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We propose a novel hybrid approach to lemmatization that enhances the seq2seqneural model with additional lemmas extracted from an external lexicon or arule-based system. During training, the enhanced lemmatizer learns both togenerate lemmas via a sequential decoder and copy the lemma characters from theexternal candidates supplied during run-time. Our lemmatizer enhanced withcandidates extracted from the Apertium morphological analyzer achievesstatistically significant improvements compared to baseline models notutilizing 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 theStanford Stanza model on the same set of languages. We also compare with othermethods of integrating external data into lemmatization and show that ourenhanced system performs considerably better than a simple lexicon extensionmethod based on the Stanza system, and it achieves complementary improvementsw.r.t. the data augmentation method.

Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources