Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
Table 2: Experiment results, Scenario Active means the query strategy used to sort instance informativeness, RandTask = Random Task Selection, Data scenario are initial percentage of labeled data, 50% means the 50:50 split, 85% means 85:15 split, and 100% means use all training data. P (Precision), R (Recall), F1 (F1 Score)
['Scenario Task', 'Scenario Active', 'Scenario Data (%)', 'Metric P', 'Metric R', 'Metric F1']
We experimented with a low-resource conversational language by varying the task scenario, active learning query strategy, and outset percentage of data seed from training data. We report our results using Precision (P), Recall (R), and the F1 score (F1) computed by exact matching of gold and predicted role spans.