Bootstrapping NLU Models with Multi-task Learning
Table 1: F1 scores (%) of domain, intent and slot classification tasks modelled by character-level and word-level models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task.
['Training size', 'Character-level model Domain', 'Character-level model Intent', 'Character-level model Slot', 'Word-level model Domain', 'Word-level model Intent', 'Word-level model Slot']
Tab. For example, when using 10K training utterances, the character-level model achieves a relative improvement of 6.62%, 11.14% and 11.95% in F1 scores of domain, intent and slot classification respectively compared to the word-level model.