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<Paper uid="J99-3003">
  <Title>Lucent Technologies Bell Laboratories</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> The call routing task is one of directing a customer's call to an appropriate destination within a call center or directly providing some simple information, such as current loan rates, on the basis of some kind of interaction with the customer. In current systems, such interaction is typically carried out via a touch-tone system with a rigid predetermined navigational menu. The primary disadvantages of navigating menus for users are the time it takes to listen to all the options and the difficulty of matching their goals to the given options. These problems are compounded by the necessity of descending a nested hierarchy of choices to zero in on a particular activity. Even requests with simple English phrasings such as I want the balance on my car loan may require users to navigate as many as four or five nested menus with four or five options each. We describe an alternative to touch-tone menus that allows users to interact with a call router in natural spoken English dialogues just as they would with a human operator.</Paragraph>
    <Paragraph position="1"> In a typical dialogue between a caller and a human operator, the operator responds to a caller request by either routing the call to an appropriate destination, or querying the caller for further information to determine where the call should be routed. Thus,</Paragraph>
    <Paragraph position="3"> (~) 1999 Association for Computational Linguistics Computational Linguistics Volume 25, Number 3 in developing an automatic call router, we select between these two options as well as a third option of sending the call to a human operator in situations where the router recognizes that to automatically handle the call is beyond its capabilities. The rest of this paper provides both a description and an evaluation of an automatic call router that consists of 1) a routing module driven by a novel application of vector-based information retrieval techniques, and 2) a disambiguation query generation module that utilizes the same vector representations as the routing module and dynamically generates queries tailored to the caller's request and the destinations with which it is consistent, based on our extension of the vector model. The overall call routing system has the following desirable characteristics: First, the training of the call router is domain independent and fully automatic, 1 allowing the system to be easily ported to new domains. Second, the disambiguation module dynamically generates queries based on caller requests and candidate destinations, allowing the system to tailor queries to specific circumstances. Third, the system is highly robust to speech recognition errors.</Paragraph>
    <Paragraph position="4"> Finally, the overall performance of the system is high, in particular when using noisy speech recognizer output. With transcription (perfect recognition), we redirect 10.2% of the calls to the operator, correctly routing 93.8% of the remainder either with or without disambiguation. With spoken input processed automatically with recognition performance at a 23% word error rate, the percentage of correctly routed calls drops by only 4%.</Paragraph>
  </Section>
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