Based on prior knowledge, the feature "other_parties" represents the involvement of other debtors or guarantors in the credit application process. We can analyze the relationship between this feature and the task of determining whether a person receives a credit using a dataset with corresponding values for both the feature and target variable.

After analyzing the dataset, we can create a dictionary to summarize the relationship between the "other_parties" feature and the task at hand. Here is an example of what the dictionary might look like:

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

In this case, for the target class "yes" (meaning the person receives credit), the possible values for the "other_parties" feature are "none" and "co applicant". On the other hand, for the target class "no" (meaning the person does not receive credit), the possible values for the "other_parties" feature are "none" and "guarantor".

Please note that the exact values in the dictionary may vary depending on the dataset used for analysis. The provided example assumes that these are the only possible values for the "other_parties" feature and that they have been observed in the dataset.