Based on prior knowledge, we can analyze the relationship between the "marital" feature and the target variable of whether the client subscribes to a term deposit.

Marital status can have an impact on a person's financial decisions and their likelihood of subscribing to a term deposit. Let's examine the possible scenarios:

1. Married individuals may have more financial responsibilities and long-term commitments, which could make them more cautious. Hence, they might be more inclined to subscribe to a term deposit. So, we can expect that for the target class "yes," there will be individuals who are married.

2. Single individuals may have fewer financial responsibilities and fewer long-term commitments compared to married individuals. Hence, they might be less likely to subscribe to a term deposit. So, we can expect that for the target class "no," there will be individuals who are single.

3. Divorced individuals could have varying financial situations depending on their circumstances. It is hard to predict how this marital status would specifically relate to the target variable. Therefore, we won't include "divorced" in the dictionary.

Based on this analysis, we can create the following dictionary:

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

Please note that the dictionary only includes the specific marital statuses that are likely to have a relationship with the target variable.