Hierarchical Encoders for Modeling and Interpreting Screenplays
Table 7: The number of distinct tag values for each tag attribute across the two datasets. Cardinalities for Scriptbase-J tag attributes are identical to Gorinski and Lapata (2018) except for the removal of one mood tag value when filtering for erroneously preprocessed scripts.
['[BOLD] Tag', '[BOLD] Internal', '[BOLD] Scriptbase-J']
The final script embedding being passed into a feedforward classifier (FFNN). As both supervised learning tasks in our evaluation are multi-label classification problems, we use a variant of a simple multi-label one-versus-rest loss, where correlations among tags are ignored. The tag sets have high cardinalities and the fractions of positive samples are inconsistent across tags L(y,z) = 1NLΣNi=1ΣLj=1yijlogσ(zij) +λj(1−yij)(1−logσ(zij)) (1) where N is the number of samples, L is the number of tag labels, y∈{0,1} is the tag label , z is the output of the FFNN, σ is the sigmoid function, and λj is the ratio of positive to negative samples (precomputed over the entire training set, since the development set is too small to tune this parameter) for the tag label indexed by j. With this loss function, we account for label imbalance without using separate thresholds for each tag tuned on the validation set. Our evaluation is based on the ScriptBase-J corpus, released by Gorinski and Lapata ScriptBase-J was used both to directly compare our approach with an implementation of the multilabel encoder architecture in Gorinski and Lapata