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Migrating millions of users from voice- and email-based customer support to a chatbot /
"At MakeMyTrip, a large number of customer care agents were dedicated to handling customer support issues over voice and email. Given the popularity of WhatsApp in India, conversational interfaces have become mainstream for the Indian consumer. MakeMyTrip decided to capitalize on this trend and...
| Clasificación: | Libro Electrónico |
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | "At MakeMyTrip, a large number of customer care agents were dedicated to handling customer support issues over voice and email. Given the popularity of WhatsApp in India, conversational interfaces have become mainstream for the Indian consumer. MakeMyTrip decided to capitalize on this trend and revamped customer support channels using a chatbot named Myra. Myra is adept at handling the languages of the Indian consumer including English, Hindi, and a mix of the two popularly known as Hinglish. Madhu Gopinathan and Sanjay Mohan explain the high-level architecture and the business impact Myra created. Myra handles user input using a natural language understanding framework that solves problems such as intent classification and slot extraction using the latest advances in deep learning. In the intent classification problem, 'Please cancel my ticket' should be mapped to a label 'full cancellation.' The intent classification model supports over 120 intents and is sophisticated enough to distinguish between fine-grained intents such as full cancellation, partial cancellation, and flight cancellation by airlines. In the slot extraction problem, from, 'Please cancel my ticket to New York, ' the value, 'New York' should be extracted to fill the destination slot. For curating the data required for training deep learning models, MakeMyTrip employed multiple methods, such as manual tagging, creating language models from existing email conversations, progressive refinement of the models, and natural language generation. In this case study, Madhu and Sanjay dive into the insights from developing deep learning models to power Myra, revamping of agent tools, and processes to support Myra and the ensuing business impact. This session is from the 2019 O'Reilly Strata Conference in New York, NY."--Resource description page |
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| Notas: | Title from title screen (viewed July 24, 2020). |
| Descripción Física: | 1 online resource (1 streaming video file (41 min., 48 sec.)) |