The effectiveness of natural language processing models relies on various factors, including the architecture, number of parameters, data used during training, and the tasks they were trained on. Recent studies indicate that models pre-trained on large corpora and fine-tuned on task-specific datasets, covering multiple tasks, can generate remarkable results across various benchmarks. We propose a new approach based on a straightforward hypothesis: improving model performance on a target task by considering other artificial tasks defined on the same training dataset. By doing so, the model can gain further insights into the training dataset and attain a greater understanding, improving efficiency on the target task. This approach differs from others that consider multiple pre-existing tasks on different datasets. We validate this hypothesis by focusing on the problem of answering yes/no questions and introducing a multi-task model that outputs a span of the reference text, serving as evidence for answering the question. The task of span extraction is an artificial one, designed to benefit the performance of the model answering yes/no questions. We acquire weak supervision for these spans, by using a pre-trained extractive question answering model, dispensing the need for costly human annotation. Our experiments, using modern transformer-based language models, demonstrate that this method outperforms the standard approach of training models to answer yes/no questions. Although the primary objective was to enhance the performance of the model in answering yes/no questions, it was discovered that span texts are a significant source of information. These spans, derived from the question reference texts, provided valuable insights for the users to better comprehend the answers to the questions. The model’s improved accuracy in answering yes/no questions, coupled with the supplementary information provided by the span texts, led to a more comprehensive and informative user experience.