论文标题
一个可解释的深度学习系统，用于自动评分提案请求
An Interpretable Deep Learning System for Automatically Scoring Request for Proposals
论文作者
论文摘要
医疗补助（美国医疗保健）内的托管护理系统使用提案请求（RFP）授予各种医疗保健和相关服务的合同。 RFP响应是竞争组织提交的非常详细的文件（数百页）以赢得合同。主题专业知识和领域知识在准备RFP响应以及对历史提交的分析方面起着重要作用。通过自然语言处理（NLP）系统对这些响应的自动分析可以减少探索历史响应所需的时间和精力，并协助编写更好的回答。我们的工作在得分RFP和论文评分模型之间取得了相似之处，同时突出了新的挑战和对可解释性的需求。典型的评分模型着重于单词级别对等级论文和其他简短文章的影响。我们提出了一个新型的基于BI-LSTM的回归模型，并对短语进行了更深入的洞察力，该短语不断影响反应的评分。我们使用广泛的定量实验来争辩我们提出的方法的优点。我们还使用人类评估者进行定性评估重要短语的影响。最后，我们介绍了一个新颖的问题陈述，可以用来进一步改善基于NLP的自动评分系统中的最新技术。
The Managed Care system within Medicaid (US Healthcare) uses Request For Proposals (RFP) to award contracts for various healthcare and related services. RFP responses are very detailed documents (hundreds of pages) submitted by competing organisations to win contracts. Subject matter expertise and domain knowledge play an important role in preparing RFP responses along with analysis of historical submissions. Automated analysis of these responses through Natural Language Processing (NLP) systems can reduce time and effort needed to explore historical responses, and assisting in writing better responses. Our work draws parallels between scoring RFPs and essay scoring models, while highlighting new challenges and the need for interpretability. Typical scoring models focus on word level impacts to grade essays and other short write-ups. We propose a novel Bi-LSTM based regression model, and provide deeper insight into phrases which latently impact scoring of responses. We contend the merits of our proposed methodology using extensive quantitative experiments. We also qualitatively asses the impact of important phrases using human evaluators. Finally, we introduce a novel problem statement that can be used to further improve the state of the art in NLP based automatic scoring systems.
