Bootstrapping NLU Models with Multi-task Learning
Table 2: F1 scores (%) of domain, intent and slot classification tasks modelled by single-task vs. multi-task 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', 'Multi-task model Domain', 'Multi-task model Intent', 'Multi-task model Slot', 'Single-task models Domain', 'Single-task models Intent', 'Single-task models Slot']
Multi-task Learning (MTL) facilitates implicit knowledge transfer among the jointly-modeled tasks via parameter sharing, which is especially beneficial in low-resource settings. Such joint modeling techniques generally improve the performance of each learning task of the model, especially the ones with higher complexity. In this experiment, we created single-task models by separating domain, intent and slot classification tasks and training on the task specific data. We then compared single-task models with the model where MTL is used. We present the experiment results where we compare single and multi task models in Tab.