Understanding Car-Speak: Replacing Humans in Dealerships
This addresses the challenge of automating car-buying interactions at dealerships, but it is incremental as it focuses on classification without demonstrating real-world impact.
The paper tackled the problem of interpreting abstract 'car-speak' language used by car-buyers to describe vehicle needs, by defining it, curating a dataset, and training classifiers for classification.
A large portion of the car-buying experience in the United States involves interactions at a car dealership. At the dealership, the car-buyer relays their needs to a sales representative. However, most car-buyers are only have an abstract description of the vehicle they need. Therefore, they are only able to describe their ideal car in "car-speak". Car-speak is abstract language that pertains to a car's physical attributes. In this paper, we define car-speak. We also aim to curate a reasonable data set of car-speak language. Finally, we train several classifiers in order to classify car-speak.