The MSIIP system for dialog state tracking challenge 5

Abstract

We present our work in Dialog State Tracking Challenge 5, the main task of which is to track dialog state on human-human conversations cross language. Firstly a probabilistic enhanced framework is used to represent sub-dialog, which consists of three parts, the input model for extracting features, the enhanced model for updating dialog state and the output model to give the tracking frame. Meanwhile, parallel language systems are proposed to overcome inaccuracy caused by machine translation for cross language testing. We also introduce a new iterative alignment method extended from our work in DSTC4. Furthermore, a slot-based score averaging method is introduced to build an ensemble by combining different trackers. Results of our DSTC5 system show that our method significantly improves tracking performance compared with baseline method.