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ISCApad Archive  »  2015  »  ISCApad #205  »  Journals  »  SPECIAL ISSUE ON DIALOGUE STATE TRACKING (Extended deadline)

ISCApad #205

Wednesday, July 08, 2015 by Chris Wellekens



Jason D. Williams, Microsoft Research
Antoine Raux, Lenovo
Matthew Henderson, Cambridge University


Submission deadline: 15 April 2015 Notification:                           26 June 2015
Final version of accepted papers due:   28 August 2015
Anticipated publication:                16 October 2015


Conversational systems are increasingly becoming a part of daily life, with examples including Apple's Siri, Google Now, 
Nuance Dragon Go, Xbox and Cortana from Microsoft, and numerous new entrants.  Many conversational systems include 
a dialogue state tracking function, which estimates relevant aspects of the interaction such as the user's goal, level of 
frustration, trust towards the system, etc, given all of the dialogue history so far.  For example, in a tourist information system,
 the dialogue state might indicate the type of business the user is searching for (pub, restaurant, coffee shop), their desired 
price range and type of food served.  Dialogue state tracking is difficult because automatic speech recognition (ASR) and 
spoken language understanding (SLU) errors are common, and can cause the system to misunderstand the user.  
At the same time, state tracking is crucial because the system relies on the estimated dialogue state to choose actions -- for 
example, which restaurants to suggest.

Most commercial systems use hand-crafted heuristics for state tracking, selecting the SLU result with the highest confidence score,
 and discarding alternatives. In contrast, statistical approaches consider many hypotheses for the dialogue state.  By exploiting 
correlations between turns and information from external data sources -- such as maps, knowledge bases, or models of past 
dialogues -- statistical approaches can overcome some SLU errors.

Although dialogue state tracking has been an active area of study for more than a decade, there has been a flurry of new work 
in the past 2 years.  This has been driven in part by the availability of common corpora and evaluation measures provided by a 
series of three research community challenge tasks called the Dialogue State Tracking Challenge.  With these resources, 
researchers are able to study dialogue state tracking without investing the time and effort required to build and operate a spoken 
dialogue system.  Shared resources also allow direct comparison of methods across research groups.  Results from the Dialogue 
State Tracking Challenge have been presented at special sessions in SIGDIAL 2013, SIGDIAL 2014, and IEEE SLT 2014.


The aim of this special issue is to provide a forum for in-depth, journal-level work on dialogue state tracking.  

This issue welcomes papers covering any topic relevant to dialogue state tracking.  Specific examples include (but are not
 limited to):

- Algorithms for dialogue state tracking, including those based on machine learning or novel heuristics
- Adaptation and learning in dialogue state tracking, for example across domains, users, usage environments, etc.
- Analyses of dialogue state tracking methods, or analyses of characteristics of dialogue that affect dialogue state tracking
- Investigations of metrics used for dialogue state tracking, including the impact of dialogue state tracking on end-to-end 
dialogue systems
- Descriptions and analyses of resources for dialogue state tracking, including corpora 
- Applications of dialogue state tracking to new domains or new settings, such as multi-modal systems

Submissions should report on new work, or substantially expand on previously published work with additional experiments, 
analysis, or important detail.  Previously-published aspects may be included but should be clearly indicated.


All data from the dialogue state tracking challenge series continues to be available for use, including the dialogue data itself,
 scripts for evaluation and baseline trackers, raw output from trackers entered in the challenges, and performance summaries. 
If your work is on dialogue state tracking for information-seeking dialogues and/or you think the data is appropriate, you are 
strongly encouraged to report results on these data, to enable comparison.

The dialogue state tracking challenge data is available here:

- Dialogue State Tracking Challenge 1:
- Dialogue State Tracking Challenge 2&3:


Papers should be submitted on the Dialogue & Discourse journal website, following instructions and formatting guidelines given

Submitted papers will be reviewed according to the Dialogue & Discourse reviewing criteria and appropriateness to the topic of 
the special issue.


Contact Jason Williams ( for further information about this call for papers.

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