Improving Lemmatization of Non-Standard Languages with Joint Learning
Table 4: Average accuracy across all 20 standard languages. Lemming and Morfette are shown aggregated by taking the best performing model per dataset.
['[EMPTY]', 'Full', 'Ambiguous', 'Unknown']
We observe that on average Sent-LM is the best model on full datasets. However, in contrast to previous results, the edit-tree approach has an advantage over all encoder-decoder models for both ambiguous and unknown tokens.