As the Internet-based crowdsourcing services become more and more popular, learning from multiple teachers or sources has received more attention of the researchers in the machine learning area. In this setting, the learning system is dealing with samples and labels provided by multiple teachers, who in common cases, are non-expert. Their labeling styles and behaviors are usually diverse, some of which are even detrimental to the learning system. Thus, simply putting them together and utilizing the algorithms designed for single-teacher scenario would be not only improper, but also damaging. The problem calls for more specific methods. Our work focuses on a case where the teachers are composed of good ones and irresponsible ones. By irresponsible, we mean the teacher who takes the labeling task not seriously and label the sample at random without inspecting the sample itself. This behavior is quite common when the task is not attractive enough and the teacher just wants to finish it as soon as possible. Sometimes, the irresponsible teachers could take a considerable part among all the teachers. If we do not take out their effects, our learning system would be ruined with no doubt. In this paper, we propose a method for picking out the good teachers with promising experimental results. It works even when the irresponsible teachers are dominating in numbers.