The motivation for these SLU features is to make use of information that the SLU module has as a result of processing the output of ASR and the current discourse context For example for utterances that follow the first utterance  the SLU module knows what task it believes the caller is trying to complete  The context-shift feature incorporates this knowledge of the discourse history  with the motivation that if it appears that the caller has changed her mind then the SLU module may have misunderstood an utterance Dialogue Manager  The function of the Dialogue Manager is to take as input the output of the SLU module decide what task the user is trying to accomplish  decide what the system will say next and update the discourse history Abella Gorin The Dialogue Manager decides whether it believes there is a single unambiguous task that the user is trying to accomplish  and how to resolve any ambiguity Features based on information that the Dialogue Manager logged about its decisions or features representing the ongoing history of the dialogue might be useful predictors of SLU
errors or task failure Some of the potentially interesting Dialogue Manager events arise due to low SLU confidence levels which lead the Dialogue Manager to reprompt the user or confirm its understanding A reprompt might be a variant of the same question that was asked before  or it could include asking the user to choose between two tasks that have been assigned similar confidences by the SLU module  For example  in the dialogue in Figure the system utterance in S  counts as a reprompt because it is a variant of the question in utterance S
The features that we extract from the Dialogue Manager are the task type label syslabel whose set of values include a value to indicate when the system had insufficient information to decide on a specific task type  the utterance id within the dialogue  utt- id the name of the prompt played to the user prompt and whether the type of prompt was a reprompt reprompt a confirmation confirm or a subdialogue prompt a superset of the reprompts and confirmation prompts subdial The sys-label feature is intended to capture the fact that some tasks may be harder than others The utt-id feature is motivated by the idea that the length of the dialogue may be important possibly in combination with other features like sys-label  The different prompt features for initial prompts  reprompts confirmation prompts and subdialogue prompts are motivated by results indicating that reprompts and confirmation prompts are frustrating for callers and that callers are likely to hyperarticulate when they have to repeat themselves  which results in ASR errors  Shriberg Walker  Kamm et al Levow Litman The discourse history features included running tallies for the number of reprompts num-reprompts number of confirmation prompts num-confirms and number of subdi alogue prompts  num-subdials   that had been played before the utterance currently being processed as well as running percentages  percent-reprompts, percent-confirms, percentsubdials The use of running tallies and percentages is based on previous work suggesting that normalized features are more likely to produce generalized predictors  Litman  Walker A feature available for identifying problematic dialogues is dial-duration Kearns that is not available for initial segments of the dialogue Hand Labelling  As mentioned above  the features obtained via hand labelling are used to provide a topline against which to compare the performance of the fully automatic fea tures   The hand labelled features include human transcripts of each user utterance   tscript a set of semantic labels that are closely related to the system task type labels human¬