TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling
Table 2: Ablation Study on DSTC4 and MixSNIPS Datasets.
['[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)']
3.5.1 Effectiveness of Intent-Slot Graph Interaction Layer In this section, we conduct experiments to verify the effectiveness of the proposed intent-slot graph interaction layer. Instead of adopting the GAT to model the interaction between the predicted intents and slot, we utilize the attention mechanism to incorporate the intents information for slot filling at token-level. After obtaining the context intent vector, we sum the vector and the hidden state of slot filling decoder to get the final slot prediction. We can see that our framework outperforms the simple attention aggregation mechanism. We attribute it to the fact that multi-layer graph attention network can automatically capture relevant intents information and better aggregate intents information for each token slot prediction.