Based on prior knowledge, the marital status of a client can potentially have an impact on whether they subscribe to a term deposit or not. Let's analyze the relationship between the feature and the task.

We can start by examining the distribution of marital status categories for each target class (yes or no).

Possible values for feature marital:
- 'married'
- 'single'
- 'divorced'

Let's assume that the target variable 'yes' indicates that the client subscribed to a term deposit, and 'no' indicates that they did not.

After analyzing the data, we find the following relationship between the feature and the task:

'no': ['married', 'single', 'divorced']
'yes': ['married', 'single', 'divorced']

As all possible values of feature marital are represented for both target classes, we can create the following dictionary:

```json
{
    "no": ["married", "single", "divorced"],
    "yes": ["married", "single", "divorced"]
}
```

Note: If there were specific values of marital status that were hard to predict or didn't have a significant impact on the target variable, we would have excluded them from the dictionary. However, in this case, we are assuming that all categories of marital status are relevant for the task.