To Test Machine Comprehension, Start by Defining Comprehension
원문 초록
Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key contributions. First, we argue that existing approaches do not adequately define comprehension; they are too unsystematic about what content is tested. Second, we present a detailed definition of comprehension -- a "Template of Understanding" -- for a widely useful class of texts, namely short narratives. We then conduct an experiment that strongly suggests existing systems are not up to the task of narrative understanding as we define it.
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.