Improving Slot Filling by Utilizing Contextual Information
Table 1: Performance of the model and baselines on the Test sets.
['[BOLD] Model', '[BOLD] SNIPS', '[BOLD] ATIS', '[BOLD] EditMe']
The proposed model outperforms all baselines in all datasets. Among all baselines, GCDT achieves best results on two out of three datasets. This superiority shows the importance of explicitly incorporating the contextual information into word representation for SF. However, the proposed model improve the performance substantially on all datasets by explicitly encouraging the consistency between word and its context in representation level and task-specific (i.e., label) level. This difficulty could be addressed by the limited number of training examples and more diversity in sentence structures in this dataset.