  Sentence-level classification and sequential labeling are two fundamental
tasks in language understanding. While these two tasks are usually modeled
separately, in reality, they are often correlated, for example in intent
classification and slot filling, or in topic classification and named-entity
recognition. In order to utilize the potential benefits from their
correlations, we propose a jointly trained model for learning the two tasks
simultaneously via Long Short-Term Memory (LSTM) networks. This model predicts
the sentence-level category and the word-level label sequence from the stepwise
output hidden representations of LSTM. We also introduce a novel mechanism of
"sparse attention" to weigh words differently based on their semantic relevance
to sentence-level classification. The proposed method outperforms baseline
models on ATIS and TREC datasets.
