A text classification model is designed to categorize sentences into one of three classes: 'Positive', 'Negative', or 'Neutral'. The model works in two stages: first, it generates a unique numerical vector representation for each input sentence. Second, a final component takes this vector and outputs a probability distribution over the three classes. During testing, you observe that for a wide variety of different input sentences, the model consistently outputs probabilities that are very close to uniform (e.g., {'Positive': 0.33, 'Negative': 0.34, 'Neutral': 0.33}). Based on this specific symptom, what is the most direct and likely cause of the problem?

Role of the Final Classification Component

A component is added to a model to predict a probability distribution over a set of predefined classes based on an input feature representation. Match each element of this component to its specific function.