The AI Trainer behind the Customer Service Robot
AI trainers, responsible for training intelligent customer service robots. Their work includes collecting customer needs from different industries, providing data annotation instruction, and designing robot dialogue logic.
“This work is a test of patience. We need to integrate a huge amount of data, slowly reduce the volume, and convert the data into useful things. It is a very long process.”
For example, to make a customer service robot for 3C products, we need to communicate with larger store front-line customer service and sales personnel in the industry, collect daily chats and issues, standardize the problems and extract industry characteristics.
In the Q&A process, the way customers ask questions can be very different. Such as “forgot your password,” customers may ask, “I lost my password,” “I don’t know what my password is,” etc., which requires trainers to standardize typical problems and then write some similar ones for model training.
After collecting the data, the trainer needs to pour the data into the system, decompose, cluster, and then label the data. Data labeling is the process of teaching artificial intelligence to recognize a particular sentence. The labeling content includes intention and word segmentation.
For example, in the sentence “The size of the phone case I bought is wrong,” the data annotator will mark it in the intention of “return and exchange.” If a sentence does not convey any intent, the annotation would be based on relevant business knowledge and usage.
Once data labeling is completed, it comes to dialogue flow design. When a customer asks a question, the chatbots need to identify accurately the scenario and intent, then search for appropriate answers from the knowledge base or present related products and services. If data annotation is for knowledge learning, then dialogue flow design is for knowledge application.
For a robot to accurately recognize an intent, a powerful model and sufficient data are required. It is said that one intent requires 50–100 sentences.
After the dialogue flow is successfully designed, it is the daily work of fixing bugs. Robots are just the intelligence piled up by AI trainers with a bunch of data. They do not have any ability to think on their own. When customers ask questions that are not included in the database, or they use too much rhetoric or the dialect accent is too heavy, the robots fail to give the feedback. Under this situation, AI trainer is required to find the problem and adjust it manually.
Many people don’t know much about AI. They think AI can reach an adult intelligence level, but it is still at a relatively early stage. Customers are always concerned about why the robot cannot answer such a simple question. In the cases that the model is not good, the initial corpus is not enough, or the algorithm is limited in the actual application process, the robots are not able to answer.
The difficulty of being an AI trainer on a platform is that you need to keep in touch and update knowledge in different industries as the application will definitely be integrated into a particular vertical field.
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