TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling
Table 1: Slot filling and intent detection results on two multi-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] DSTC4 Slot (F1)', '[BOLD] DSTC4 Intent (F1)', '[BOLD] DSTC4 Intent (Acc)', '[BOLD] DSTC4 Overall (Acc)', '[BOLD] MixSNIPS Slot (F1)', '[BOLD] MixSNIPS Intent (F1)', '[BOLD] MixSNIPS Intent (Acc)', '[BOLD] MixSNIPS Overall (Acc)']
Following Goo et al. The performance of intent prediction are evaluated by accuracy and marco F1 score. The sentence-level semantic frame parsing are evaluated by overall accuracy which represents all metrics are right in an utterance.