Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding
Table 3: Impact of the context length on the F1-score (%)
['Context length', '5', '7', '9', '10', '11']
First, the impact of the context length on the final performance was explored. The number of parameters remained unchanged when reducing or increasing the context length. Short context means information loss while a long context length potentially adds noise to the input of the model. Increasing the context length to 10 and 11, however, decreased the results slightly but the F1-scores stayed quite stable around 95.5%. This confirms our hypothesis that a longer context adds noise to the input while the model is still able to extract the important information for slot prediction.