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 presenting 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 intradomain clustering method that automatically discovers shared slots from training data. The effectiveness of our proposed approaches is demonstrated in an SLU application that involves three different yet related tasks.