# Automatically Training A Problematic Dialogue Predictor For A Spoken Dialogue System

Marilyn A. Walker Irene Langkilde-Geary Helen  Wright  Hastie Jerry Wright Allen Gorin AT&T Shannon Laboratory 180 Park Ave., Bldg 103, Room E103 Florham Park, NJ 07932 WALKER@RESEARCH.ATT.COM
ILANGKIL@ISI.EDU
H H A ST I E @ R E S E A R C H . A T T . C O M
JW RIGHT @ RESEA RCH. ATT . CO M
A LGOR@ RESEA RCH. ATT. COM
Abstract How May I Help You'M

## 1. Introduction

Spoken dialogue systems promise efficient and natural access to a large variety of information sseen to the state of the state of the state of the state of the state of the state of the state of the state.

particular function through a statement such as  Say credit card  collect or person to person" are saving companies millions of dollars per year Deployed systems and research prototypes exist for applications such as personal email and calendars travel and restaurant information and personal banking Baggia Castagneri    Danieli Walker   Fromer Seneff Zue Polifroni Pao Hetherington Goddeau Narayanan Glass Sanderman Sturm den Os Boves   Cremers Chu Carroll Carpenter inter alia  Yet there are still many research challenges  current systems are limited in the interaction they support and brittle in many respects This paper investigates methods by which spoken dialogue systems can learn to support more natural interaction on the basis of their previous experience One way that current spoken dialogue systems are quite limited is in their strategies for detecting and repairing problems that arise in conversation such as misunderstandings due to speech recognition error or misinterpretation  If a problem can be detected  the system can either transfer the call to a human customer care agent or modify its dialogue strategy in an attempt to