When is multitask learning effective? Semantic sequence prediction under varying data conditions
Table 5: Comparison default hierarchical systems using a lower-level bi-LSTM for characters (BL w+c) versus system using only words (w).
['[EMPTY]', 'BL ( [ITALIC] w+ [ITALIC] c)', 'Δonly  [ITALIC] w']
Throughout the article we used the default network structure which includes a lower-level bi-LSTM at the character level. However, we hypothesize that the character features are not equally important for all tasks. In fact, if we disable the character features, making the system only depend on word information (cf. , we observe that two of the tasks (albeit the ones with the overall lowest performance) increase their performance in about 2.5 points, namely Mpqa and Frames. For the other two tasks we observe drops up to a maximum of 8-points for Ner. Character embeddings are informative for Ner, because they approximate the well-known capitalization features in traditional models. Character features are not informative for tasks that are more dependent on word identity (like Frames), but are indeed useful for tasks where parts of the word can be informative, such as Pos or Ner.