2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDHLT-12.5
Paper Title PROGRESSIVE DIALOGUE STATE TRACKING FOR MULTI-DOMAIN DIALOGUE SYSTEMS
Authors Jiahao Wang, Sun Yat-sen University, China; Minqian Liu, South China University of Technology, China; Xiaojun Quan, Sun Yat-sen University, China
SessionHLT-12: Language Understanding 4: Semantic Understanding
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-DIAL] Discourse and Dialog
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract There are two critical observations in multi-domain dialogue state tracking (DST) that have been ignored in most existing work. First, the number of triples (domain-slot-value) in dialogue states generally increases with the growth of dialogue turns. Second, even though dialogue states are accumulating, the difference between two adjacent turns is steadily minor. To model the two observations, we propose to divide the task into two successive procedures: progressive domain-slot tracking and shrunk value prediction. Specifically, domain-slot pairs are first modeled in a multi-level structure that can be predicted progressively based on previous turns. Then, we employ a generative approach to producing dialogue values for the predicted, rather than for all possible, domain-slot pairs. This divide-and-conquer approach not only enables parallelization for predicting domain-slot pairs, but also reduces the number of domain-slot candidates significantly for value prediction. Experimental results on the MultiWOZ datasets confirm that our methodology achieves very favourable improvement over baseline models.