A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
Table 2: Slot filling and intent detection results on two datasets. The numbers with * indicate that the improvement of our model over all baselines is statistically significant with p<0.05 under t-test.
['[BOLD] Model', '[BOLD] SNIPS Slot (F1)', '[BOLD] SNIPS Intent (Acc)', '[BOLD] SNIPS Overall (Acc)', '[BOLD] ATIS Slot (F1)', '[BOLD] ATIS Intent (Acc)', '[BOLD] ATIS Overall (Acc)']
Following \newcitegoo2018slot, we evaluate the SLU performance of slot filling using F1 score and the performance of intent prediction using accuracy, and sentence-level semantic frame parsing using overall accuracy. To see the role of intent information for SLU tasks intuitively, we also present the result when using the gold intent information. From the result, we can see that further leveraging better intent information will lead to better slot filling performance. The result also verifies our assumptions that intent information can be used for guiding the slots prediction.