论文标题
语法意识到的BERT，用于在课程框架中识别良好的查询
A Syntax Aware BERT for Identifying Well-Formed Queries in a Curriculum Framework
论文作者
论文摘要
形成良好的查询定义为以查询方式和正确的疑问，拼写和语法的方式制定的查询。虽然确定良好的查询是一项重要的任务，但很少有作品尝试解决它。在本文中，我们建议基于变压器的语言模型 - 从变压器（BERT）到此任务的双向编码器表示。我们进一步吸收了来自早期作品灵感的语音信息信息。此外，我们还在多个课程设置中训练该模型以改善性能。对任务的课程学习是通过婴儿步骤和一项通行技术进行实验的。提议的架构在任务上表现出色。最佳方法的准确性为83.93％，表现优于先前的最先前，达到75.0％，达到了近似人类上限的88.4％。
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%.
