Question answer generation using Natural Language Processing models is ubiquitous in the world around us. It is used in many use cases such as the building of chat bots, suggestive prompts in google search and also as a way of navigating information in banking mobile applications etc. It is highly relevant because a frequently asked questions (FAQ) list can only have a finite amount of questions but a model which can perform question answer generation could be able to answer completely new questions that are within the scope of the data. This helps us to be able to answer new questions accurately as long as it is a relevant question. In commercial applications, it can be used to increase customer satisfaction and ease of usage. However a lot of data is generated by humans so it is susceptible to human error and this can adversely affect the model's performance and we are investigating this through our work

翻译：利用自然语言处理模型进行问答生成在我们周围世界中无处不在。它被广泛应用于诸多场景，例如聊天机器人的构建、谷歌搜索中的建议性提示，以及银行移动应用程序中信息导航等。由于常见问题列表只能包含有限数量的问题，而能够进行问答生成的模型则能回答数据范围内全新的问题，这使得该技术极具相关性。只要问题在相关范围内，这便能帮助我们准确回答新问题。在商业应用中，它可用于提升客户满意度和使用便捷性。然而，大量数据由人类生成，因此容易受到人为错误的影响，这可能对模型性能产生不利影响，我们正通过本研究对此进行探究。