With One Voice: Composing a Travel Voice Assistant from Repurposed Models

Shachaf Poran, Gil Amsalem, Amit Beka, Dmitri Goldenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for Booking.com. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional Machine-Learning product can be built from existing models.

Original languageEnglish
Title of host publicationWWW 2022 - Companion Proceedings of the Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Pages383-387
Number of pages5
ISBN (Electronic)9781450391306
DOIs
StatePublished - 25 Apr 2022
Externally publishedYes
Event31st ACM Web Conference, WWW 2022 - Virtual, Online, France
Duration: 25 Apr 2022 → …

Publication series

NameWWW 2022 - Companion Proceedings of the Web Conference 2022

Conference

Conference31st ACM Web Conference, WWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period25/04/22 → …

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • Machine Learning Architecture
  • Recommendation
  • Search
  • Voice

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