TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling
Table 3: Slot filling and intent detection results on two single-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test.
['[BOLD] Model', '[BOLD] ATIS Slot (F1)', '[BOLD] ATIS Intent (F1)', '[BOLD] ATIS Intent (Acc)', '[BOLD] ATIS Overall (Acc)', '[BOLD] SNIPS Slot (F1)', '[BOLD] SNIPS Intent (F1)', '[BOLD] SNIPS Intent (Acc)', '[BOLD] SNIPS Overall (Acc)']
We conduct experiments on two public single-intent benchmark to evaluate the generalizability of the proposed framework. We compare our model with the single-intent state-of-the-art models including SF-ID, Stack-Propagation and multi-intent model including Joint Multiple ID-SF. From the table, we can see that our model significantly outperforms all the compared baselines and achieves the state-of-the-art performance. We obtain 0.7% improvement on Overall (Acc) and 0.4% improvement on Overall (Acc) on ATIS and SNIPS dataset, respectively. This demonstrates the generalizability and effectiveness of our framework whether handling multi-intent or single-intent SLU.