Multi-task Learning for Japanese Predicate Argument Structure Analysis
Table 4: F1 scores on the PASA test set with respect to attributive modifications.
['[EMPTY]', 'ALL', 'NOM', 'ACC', 'DAT']
In Example (f), the Multi-ALL+DEP model correctly predicted the answer, but the Multi-ALL model failed. Specifically, Multi-ALL+DEP correctly predicted that the ACC argument is yakuwari 役割 “role,” which is dependent on hatasu 果たす “play.” However, the Multi-ALL incorrectly predicted that the ACC argument is kaimei 解明 “solution.” Multi-ALL+DEP considerably outperformed Multi-ALL for all cases using dependency features. Therefore, these results suggest that the dependency type feature is effective for PASA with respect to attributive modifications.