  This paper investigates the framework of encoder-decoder with attention for
sequence labelling based spoken language understanding. We introduce
Bidirectional Long Short Term Memory - Long Short Term Memory networks
(BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep
learning. In the sequence labelling task, the input and output sequences are
aligned word by word, while the attention mechanism cannot provide the exact
alignment. To address this limitation, we propose a novel focus mechanism for
encoder-decoder framework. Experiments on the standard ATIS dataset showed that
BLSTM-LSTM with focus mechanism defined the new state-of-the-art by
outperforming standard BLSTM and attention based encoder-decoder. Further
experiments also show that the proposed model is more robust to speech
recognition errors.
