Improving Lemmatization of Non-Standard Languages with Joint Learning
Table 5: Overall accuracy of Sent and Sent-LM models and a Majority baseline on 5 probing tasks and actual number of languages per morphological category. All differences over Sent except for Case are significant at p<0.05. Support is shown in terms of number of languages exhibiting such grammatical distinctions.
['[EMPTY]', 'Pos', 'Dep', 'Gender', 'Case', 'Num']
Linzen et al. . Using the same train-dev-test splits from the current study, we exploit additional POS, Number, Gender, Case and syntactic function (Dep) annotations provided in the UD corpora and compare the ability of the representations extracted by Sent and Sent-LM to predict these labels. The classifier trained with Sent-LM outperforms the one with Sent on all considered labeling tasks, confirming the efficiency of the LM loss at extracting better representations.