Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension

TL;DR

This paper presents an end-to-end machine reading comprehension framework for automatically evaluating task requirement responses in English tests, accurately detecting and locating responses to specific requirements.

Contribution

It introduces a novel MRC-based approach with three modules for requirement detection and response localization, improving automatic TR evaluation accuracy.

Findings

Achieved 0.93 accuracy in requirement detection

Achieved 0.85 F1 score in response localization

Framework effectively supports automatic TR assessment

Abstract

Task requirements (TRs) writing is an important question type in Key English Test and Preliminary English Test. A TR writing question may include multiple requirements and a high-quality essay must respond to each requirement thoroughly and accurately. However, the limited teacher resources prevent students from getting detailed grading instantly. The majority of existing automatic essay scoring systems focus on giving a holistic score but rarely provide reasons to support it. In this paper, we proposed an end-to-end framework based on machine reading comprehension (MRC) to address this problem to some extent. The framework not only detects whether an essay responds to a requirement question, but clearly marks where the essay answers the question. Our framework consists of three modules: question normalization module, ELECTRA based MRC module and response locating module. We extensively explore state-of-the-art MRC methods. Our approach achieves 0.93 accuracy score and 0.85 F1 score on a real-world educational dataset. To encourage reproducible results, we make our code publicly available at \url{https://github.com/aied2021TRMRC/AIED_2021_TRMRC_code}.