Improving Lemmatization of Non-Standard Languages with Joint Learning
Table 1: Average accuracy across historical languages. Lemming and Morfette are shown aggregated by taking the best performing model per dataset.
['[EMPTY]', 'Full', 'Ambiguous', 'Unknown']
In 4 cases (cga, cgl, crm and gml), Lemming failed to converge due to memory requirements exceeding 250G RAM due to the large amount of edit-trees. Following Søgaard et al. Sent-LM is the best performing model with a relative improvement of 7.9% (p