12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Multi-Task Learning for Spoken Language Understanding with Shared Slots

Xiao Li (1), Ye-Yi Wang (1), Gokhan Tur (2)

(1) Microsoft Research, USA
(2) Microsoft Speech Labs, USA

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 intra-domain 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.

Full Paper

Bibliographic reference.  Li, Xiao / Wang, Ye-Yi / Tur, Gokhan (2011): "Multi-task learning for spoken language understanding with shared slots", In INTERSPEECH-2011, 701-704.