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Uber AI

COTA: Improving Uber Customer Care with NLP & Machine Learning

3 January 2018 / Global
Featured image for COTA: Improving Uber Customer Care with NLP & Machine Learning
Figure 1: Uber’s customer in-app support flow presents users with an intuitive and easy-to-use interface that highlights trip details and suggests issue types to help with routing.
Figure 2: The COTA system architecture is composed of a seven-step workflow.
Figure 3: The NLP pipeline we built for ticket issue identification and solution selection is composed of three distinct steps: preprocessing, feature engineering, and computation via pointwise ranking algorithm.
Figure 4: a) Topic modeling: we use TF-IDF and LSA to extract topics from rich text data in customer support tickets processed by our customer support platform. b) Feature engineering: all the solutions and tickets are mapped to the topic vector space, and cosine similarity between solution and ticket pairs are computed.
Figure 5: Pointwise ranking is 25 percent more accurate than the multi-class classification on the solution selection task.
Figure 6: A comparison between the ability of our deep learning model and classical model (random forest) to identify issue type reveals that the deep learning model achieves greater data coverage and accuracy.
Huaixiu Zheng

Huaixiu Zheng

Huaixiu Zheng is a senior data scientist at Uber, working on projects in the domains of deep learning, reinforcement learning, natural language processing and conversational AI systems.

Yi-Chia Wang

Yi-Chia Wang

Yi-Chia Wang is a research scientist at Uber AI, focusing on the conversational AI. She received her Ph.D. from the Language Technologies Institute in School of Computer Science at Carnegie Mellon University. Her research interests and skills are to combine language processing technologies, machine learning methodologies, and social science theories to statistically analyze large-scale data and model human-human / human-bot behaviors. She has published more than 20 peer-reviewed papers in top-tier conferences/journals and received awards, including the CHI Honorable Mention Paper Award, the CSCW Best Paper Award, and the AIED Best Student Paper Nomination.

Piero Molino

Piero Molino

Piero is a Staff Research Scientist in the Hazy research group at Stanford University. He is a former founding member of Uber AI where he created Ludwig, worked on applied projects (COTA, Graph Learning for Uber Eats, Uber’s Dialogue System) and published research on NLP, Dialogue, Visualization, Graph Learning, Reinforcement Learning and Computer Vision.

Posted by Huaixiu Zheng, Yi-Chia Wang, Piero Molino

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