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: In connection with the acquisition and securitization of residential whole loans, we rely on unaffiliated servicing companies to service and manage the mortgages underlying our non-Agency mortgage-backed securities and our residential whole loans. If a servicer is not vigilant in seeing that borrowers make their required monthly payments, borrowers may be less likely to make these payments, resulting in a higher frequency of default. If a servicer takes longer to liquidate non-performing mortgages, our losses related to those loans may be higher than originally anticipated. Any failure by servicers to service these mortgages and related real estate owned (“REO”) properties could negatively impact the value of these investments and our financial performance. In addition, while we have contracted, and will continue to contract, with unaffiliated servicing companies to carry out the actual servicing of the loans we purchase together with the related MSR (including all direct interface with the borrowers), we are nevertheless ultimately responsible, vis-à-vis the borrowers and state and federal regulators, for ensuring that the loans are serviced in accordance with the terms of the related notes and mortgages and applicable law and regulation. In light of the current regulatory environment, such exposure could be significant even though we might have contractual claims against our servicers for any failure to service the loans to the required standard.
Assistant: