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.

提出了一种基于注意力机制的神经网络模型，用于同时执行关键步骤（意图检测、填充构構）进行语音理解和对话系统处理。该模型基于编码 - 解码框架，将对齐信息的注意力融合到了模型中，其表现优于传统模型。