Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
This work improves accuracy for speech and dialog systems, but it is incremental as it adapts existing attention mechanisms to a specific task.
The authors tackled joint intent detection and slot filling for speech understanding by proposing attention-based RNN models, achieving state-of-the-art results on the ATIS benchmark with a 0.56% absolute error reduction in intent detection and 0.23% gain in slot filling.
Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. We explore different strategies in incorporating this alignment information to the encoder-decoder framework. Learning from the attention mechanism in encoder-decoder model, we further propose introducing attention to the alignment-based RNN models. Such attentions provide additional information to the intent classification and slot label prediction. Our independent task models achieve state-of-the-art intent detection error rate and slot filling F1 score on the benchmark ATIS task. Our joint training model further obtains 0.56% absolute (23.8% relative) error reduction on intent detection and 0.23% absolute gain on slot filling over the independent task models.