Short phrases without object

Adding phrases to the NLU model that include both an object (e.g., “I want to change the contact phone number”) and those that focus solely on the action (e.g., “I want to change”) has its advantages and can improve the overall performance of the model. Each type of phrase provides the model with different information, which helps in better intent recognition.

Advantages of Adding Phrases Containing an Object:

1. Better Context: Phrases containing an object provide complete context, allowing the model to accurately understand what action is being requested. For example, "I want to change the contact phone number" clearly indicates what the user wants to change.
2. Reduced Ambiguity: An object in the phrase reduces ambiguity, which is particularly important when different intents might have similar action phrases. For instance, the phrase "I want to change" could relate to various objects (phone number, address, policy, etc.), but "I want to change the contact phone number" is specific and unambiguous.
3. Improved Accuracy: A model trained on phrases with objects can achieve higher accuracy because it better understands which actions are being performed on which objects.

Advantages of Adding Phrases Containing Only the Action:

1. Intent Recognition Without an Object: Phrases containing only the action are useful when the user does not provide complete context, and the model must infer the intent. For example, if a user inputs "I want to change", the model should be able to ask for additional details or assign the most common intent.
2. Increased Flexibility: A model that also learns from phrases without objects becomes more flexible and better handles the variety of user queries, as users may not always provide complete phrases.
3. Preparation for Different Scenarios: Users might use shortcuts or incomplete sentences, so action phrases help the model prepare for such situations.

The best approach is to incorporate both types of phrases into the model: those containing objects and those containing only actions.

1. Balancing the Dataset: Ensure that the training dataset includes an appropriate number of phrases containing objects as well as phrases containing only actions. Balancing the number of both types of phrases will help the model learn to recognize intents in various contexts.
2. Data Augmentation: Consider using data augmentation techniques to generate more examples with different objects and actions. For instance, for the phrase “I want to change”, add various objects such as “address”, “phone number”, “policy”, etc.
3. Example Analysis: Regularly analyze examples and model performance to ensure that phrases containing only actions are correctly recognized. If you notice that the model is struggling, add more examples of such phrases.

Adding phrases containing both an action and an object, as well as phrases containing only an action, is crucial for building a versatile and accurate NLU system. Phrases with objects provide full context and reduce ambiguity, while phrases with only actions enhance the model's flexibility and ability to handle incomplete user queries. Balancing both types of phrases, along with regular analysis and refinement of the dataset, will help achieve the best results.