An Interpretable Deep Learning System for Automatically Scoring Request for Proposals

TL;DR

This paper introduces an interpretable Bi-LSTM regression model for automatically scoring RFP responses in Medicaid, providing insights into influential phrases and demonstrating its effectiveness through quantitative and qualitative evaluations.

Contribution

It presents a novel interpretable deep learning approach for RFP response scoring, addressing the need for transparency and deeper phrase impact analysis in NLP models.

Findings

The model achieves competitive scoring accuracy.

Qualitative analysis highlights key phrases influencing scores.

Introduces a new problem statement for future NLP scoring research.

Abstract

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.