A well formed query is defined as a query which is formulated in the manner of an inquiry, and with correct interrogatives, spelling and grammar. While identifying well formed queries is an important task, few works have attempted to address it. In this paper we propose transformer based language model - Bidirectional Encoder Representations from Transformers (BERT) to this task. We further imbibe BERT with parts-of-speech information inspired from earlier works. Furthermore, we also train the model in multiple curriculum settings for improvement in performance. Curriculum Learning over the task is experimented with Baby Steps and One Pass techniques. Proposed architecture performs exceedingly well on the task. The best approach achieves accuracy of 83.93%, outperforming previous state-of-the-art at 75.0% and reaching close to the approximate human upper bound of 88.4%.

翻译：完善的查询被定义为以调查方式,以正确的讯问、拼写和语法方式编制的查询。虽然确定完善的查询是一项重要任务,但很少有工作试图解决这个问题。在本文中,我们提出了基于变异器的语言模型――变异器的双向编码器演示,以完成这项任务。我们进一步以早期作品所启发的部分语音信息向BERT灌输。此外,我们还在多个课程设置中培训该模型,以改进绩效。课程学习与任务有关的内容正在试验“婴儿步骤”和“一次传递”技术。拟议的结构在任务上表现极好。最佳方法达到83.93%的准确率,比先前的艺术水平高75.0%,接近88.4%的近距离。