Based on my prior knowledge, the feature "other_parties" represents whether or not the applicant has any other debtors or guarantors. Let's analyze the relationship between this feature and the task of determining whether the person receives a credit.

From a logical standpoint, having a guarantor or a co-applicant can increase the likelihood of a person receiving credit as they provide additional security for the loan. On the other hand, if the applicant has no other debtors or guarantors, it could potentially decrease the chances of them receiving credit due to higher perceived risk for the lender.

To confirm this relationship, we would need to analyze the data and calculate the proportion of people receiving credit for each category of the "other_parties" feature.

Now, let's create the dictionary:

```json
{
	"yes": ["guarantor", "co applicant"], 
	"no": ["none"]
}
```

Here, we include the categories "guarantor" and "co applicant" for the "yes" class, implying that having a guarantor or a co-applicant increases the chances of receiving credit. The "no" class includes the category "none", indicating that if the applicant has no other debtors or guarantors, they are less likely to receive credit.

Please note that this analysis is based on prior knowledge and assumptions, and it would be valuable to confirm these relationships using actual data analysis.