论文标题
基于变压器的方法，用于改善应用程序审核响应生成
A Transformer-Based Approach for Improving App Review Response Generation
论文作者
论文摘要
移动应用程序通过提供各种功能（例如消息传递和游戏）来成为人们日常生活中不可或缺的一部分。应用程序开发人员会尽力确保在应用程序开发和维护期间的用户体验，以提高应用程序平台上应用程序的评级并吸引更多用户下载。先前的研究表明，回应用户的评论往往会积极改变他们对应用程序的态度。回复的用户更有可能更新给定的评分。但是，阅读和响应每个用户评论对于开发人员来说并不是一件容易的事，因为受欢迎的应用程序每天都会收到大量的评论。因此，需要自动化工具进行回复。 为了满足上述需求，本文介绍了一种名为TRRGEN的基于变压器的方法，以自动生成对给定用户评论的响应。 TRRGEN提取应用程序的类别，评分和评论文本作为输入功能。通过调整基于变压器的模型，TRRGEN可以为新评论生成适当的答复。对现实世界数据集的全面实验和分析表明，所提出的方法可以为用户的评论产生高质量的答复，并且在任务上的最先进方法明显超过了当前的最新方法。对生成的答复的手动验证结果进一步证明了拟议方法的有效性。
Mobile apps are becoming an integral part of people's daily life by providing various functionalities, such as messaging and gaming. App developers try their best to ensure user experience during app development and maintenance to improve the rating of their apps on app platforms and attract more user downloads. Previous studies indicated that responding to users' reviews tends to change their attitude towards the apps positively. Users who have been replied are more likely to update the given ratings. However, reading and responding to every user review is not an easy task for developers since it is common for popular apps to receive tons of reviews every day. Thus, automation tools for review replying are needed. To address the need above, the paper introduces a Transformer-based approach, named TRRGen, to automatically generate responses to given user reviews. TRRGen extracts apps' categories, rating, and review text as the input features. By adapting a Transformer-based model, TRRGen can generate appropriate replies for new reviews. Comprehensive experiments and analysis on the real-world datasets indicate that the proposed approach can generate high-quality replies for users' reviews and significantly outperform current state-of-art approaches on the task. The manual validation results on the generated replies further demonstrate the effectiveness of the proposed approach.
