Jointly predicting dialog act and named entity for spoken language understanding

Spoken language understanding (SLU) addresses the problem of mapping natural language speech into semantic frame for structure encoding of its meaning. Most of the SLU systems separate out the dialog act (DA) identification from the named entity (NE) recognition to generate the semantic frames. In previous works, these two subtasks are treated by independent or cascaded approaches. In the cascaded systems, however, DA and NE influence only to one side, rather than to both sides. In this paper, we develop a new joint SLU model with a triangular-chain conditional random field (CRF) to encode inter-dependence between DA and NE. On four real dialog data, we show that our joint approach outperforms both independent and cascaded approaches.