  Initial fault detection and diagnostics are imperative measures to improve
the efficiency, safety, and stability of vehicle operation. In recent years,
numerous studies have investigated data-driven approaches to improve the
vehicle diagnostics process using available vehicle data. Moreover, data-driven
methods are employed to enhance customer-service agent interactions. In this
study, we demonstrate a machine learning pipeline to improve automated vehicle
diagnostics. First, Natural Language Processing (NLP) is used to automate the
extraction of crucial information from free-text failure reports (generated
during customers' calls to the service department). Then, deep learning
algorithms are employed to validate service requests and filter vague or
misleading claims. Ultimately, different classification algorithms are
implemented to classify service requests so that valid service requests can be
directed to the relevant service department. The proposed model- Bidirectional
Long Short Term Memory (BiLSTM) along with Convolution Neural Network (CNN)-
shows more than 18\% accuracy improvement in validating service requests
compared to technicians' capabilities. In addition, using domain-based NLP
techniques at preprocessing and feature extraction stages along with CNN-BiLSTM
based request validation enhanced the accuracy ($>25\%$), sensitivity
($>39\%$), specificity ($>11\%$), and precision ($>11\%$) of Gradient Tree
Boosting (GTB) service classification model. The Receiver Operating
Characteristic Area Under the Curve (ROC-AUC) reached 0.82.
