Intent detection and slot filling are two main tasks for building a spoken
language understanding(SLU) system. Multiple deep learning based models have
demonstrated good results on these tasks . The most effective algorithms are
based on the structures of sequence to sequence models (or "encoder-decoder"
models), and generate the intents and semantic tags either using separate
models or a joint model. Most of the previous studies, however, either treat
the intent detection and slot filling as two separate parallel tasks, or use a
sequence to sequence model to generate both semantic tags and intent. Most of
these approaches use one (joint) NN based model (including encoder-decoder
structure) to model two tasks, hence may not fully take advantage of the
cross-impact between them. In this paper, new Bi-model based RNN semantic frame
parsing network structures are designed to perform the intent detection and
slot filling tasks jointly, by considering their cross-impact to each other
using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a
decoder achieves state-of-the-art result on the benchmark ATIS data, with about
0.5$\%$ intent accuracy improvement and 0.9 $\%$ slot filling improvement.

通过设计新的双模型基于 RNN 的语义框架解析网络结构，使用两个相关的双向 LSTMs 联合执行意图检测和槽位填充任务，取得了关于意图准确性提高了约 0.5％ 和槽位填充提高了 0.9％ 的最新成果。