Bootstrapping NLU Models with Multi-task Learning
Table 3: F1 scores (%) of domain, intent and slot classification tasks modelled by random vs. fastText-init 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', 'FastText-init model Domain', 'FastText-init model Intent', 'FastText-init model Slot', 'Randomly initialized model Domain', 'Randomly initialized model Intent', 'Randomly initialized model Slot']
We present the comparison of the random vs FastText-init models in Tab. The FastText-init model performs better than the randomly initialized model for all NLU tasks and on all sizes of training data. We observe the biggest relative gains in the slot classification task which relies on robust word-embeddings, and benefits the most from pre-training even when trained with large amounts of data. This also shows that using character embeddings does not prevent us from taking advantage of the rich literature on word embeddings and using existing word embeddings to improve model performance.