Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension
원문 초록
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}.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehension
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
토론
이 논문과 연결된 포럼 글이 아직 없습니다.