Provided in the present invention are a scalable field human-machine dialogue system state tracking method and device. The method comprises: establishing a state tracking model, employing a slot-value model to determine a current dialogue state; splitting a training data feature set, collecting each round of dialogues, and collecting feature items therein, splitting each feature item; preparing context information; and machine learning method-based model training. Because independent prediction is performed with respect to each feature by the state tracking model in the method, and the range for feature values is not limited, dynamic changes to the range for feature values is supported, thus obviating the need to retrain a language comprehension model during a service upgrade process, and enhancing the scalability of a field human-machine dialogue system.
SCALABLE FIELD HUMAN-MACHINE DIALOGUE SYSTEM STATE TRACKING METHOD AND DEVICE