System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: For certain types of loans underlying securities we may own or acquire, the loan interest rate or borrower payment rate may increase over time, increasing the potential for default. For example, securities may be backed by residential real estate loans that have negative amortization features. The rate at which interest accrues on these loans may change more frequently or to a greater extent than payment adjustments on an adjustable-rate loan, and adjustments of monthly payments may be subject to limitations or may be limited by the borrower’s option to pay less than the full accrual rate. As a result, the amount of interest accruing on the remaining principal balance of the loans at the applicable adjustable mortgage loan rate may exceed the amount of the monthly payment. To the extent we are exposed to it, this is particularly a risk in a rising interest rate environment. Negative amortization occurs when the resulting excess (of interest owed over interest paid) is added to the unpaid principal balance of the related adjustable mortgage loan. For certain loans that have a negative amortization feature, the required monthly payment is increased after a specified number of months or after a maximum amount of negative amortization has occurred in order to fully amortize the loan by the end of its original term. Other negative amortizing loans limit the amount by which the monthly payment can be increased, which results in a larger final payment at maturity. As a result, negatively amortizing loans have performance characteristics similar to those of balloon-payment loans. Negative amortization may result in increases in delinquencies, loan loss severity, and loan defaults, which may, in turn, result in payment delays and credit losses on our investments. Other types of loans and investments to which we are exposed, such as hybrid loans and adjustable-rate loans, may also have greater credit risk than more traditional amortizing fixed-rate mortgage loans.
Assistant: