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 language | English |
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Title of host publication | WWW 2022 - Companion Proceedings of the Web Conference 2022 |
Publisher | Association for Computing Machinery, Inc |
Pages | 383-387 |
Number of pages | 5 |
ISBN (Electronic) | 9781450391306 |
DOIs | |
State | Published - 25 Apr 2022 |
Externally published | Yes |
Event | 31st ACM Web Conference, WWW 2022 - Virtual, Online, France Duration: 25 Apr 2022 → … |
Publication series
Name | WWW 2022 - Companion Proceedings of the Web Conference 2022 |
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Conference
Conference | 31st ACM Web Conference, WWW 2022 |
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Country/Territory | France |
City | Virtual, Online |
Period | 25/04/22 → … |
Bibliographical note
Publisher Copyright:© 2022 ACM.
Keywords
- Machine Learning Architecture
- Recommendation
- Search
- Voice