Multi-task learning for spoken language understanding with shared slots

This paper addresses the problem of learning multiple spoken language understanding (SLU) tasks that have overlapping sets of slots. In such a scenario, it is possible to achieve better slot filling performance by learning multiple tasks simultaneously, as opposed to learning them independently. We focus on pre-senting a number of simple multi-task learning algorithms for slot filling systems based on semi-Markov CRFs, assuming the knowledge of shared slots. Furthermore, we discuss an intra-domain clustering method that automatically discovers shared slots from training data. The effectiveness of our proposed ap-proaches is demonstrated in an SLU application that involves three different yet related tasks. Index Terms: spoken language understanding, slot filling, multi-task learnin