Causal analysis of task completion errors in spoken music retrieval interactions

LREC 2012  ·  Sunao Hara, Norihide Kitaoka, Kazuya Takeda ·

In this paper, we analyze the causes of task completion errors in spoken dialog systems, using a decision tree with N-gram features of the dialog to detect task-incomplete dialogs. The dialog for a music retrieval task is described by a sequence of tags related to user and system utterances and behaviors... The dialogs are manually classified into two classes: completed and uncompleted music retrieval tasks. Differences in tag classification performance between the two classes are discussed. We then construct decision trees which can detect if a dialog finished with the task completed or not, using information gain criterion. Decision trees using N-grams of manual tags and automatic tags achieved 74.2{\%} and 80.4{\%} classification accuracy, respectively, while the tree using interaction parameters achieved an accuracy rate of 65.7{\%}. We also discuss more details of the causality of task incompletion for spoken dialog systems using such trees. read more

PDF Abstract
No code implementations yet. Submit your code now

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here