A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
Table 4: The SLU performance on BERT-based model on two datasets.
['[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)']
From the table, the BERT model performs remarkably well on both two datasets and achieves a new state-of-the-art performance, which indicates the effectiveness of a strong pre-trained model in SLU tasks. We attribute this to the fact that pre-trained models can provide rich semantic features, which can help to improve the performance on SLU tasks. In addition, our model + BERT outperforms the BERT SLU Chen et al. It demonstrates that our framework is effective with BERT. Especially, we also conduct experiments of intent detection task and slot filling separately based on BERT model. For intent detection, we put the special [CLS] word embedding into a classification layer to classify the intent. For slot filling, we feed the final hidden representation hBERTi ∈ Rd for each token From the result, we can see that the slot filling (F1) and intent detection accuracy (Acc) is lower than our joint model based on BERT, which again demonstrates the effectiveness of exploiting the relationship between these two tasks.