Overview of CLEF QA Entrance Exams Task 2014

By Anselmo Peñas, Yusuke Miyao, Álvaro Rodrigo, Eduard H. Hovy, Noriko Kando. 2014

24 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

RACE: Large-scale ReAding Comprehension Dataset From Examinations (2017, methodology): “, 2013) (containing 500 passages and 2000 questions) and several others (Peñas et al., 2014; Rodrigo et al., 2015; Khashabi et al., 2016; Shibuki et al., 2014), the usefulness of those datasets is significantly restricted due to their small sizes, especially not suitable for training powerful deep n”
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension (2019, neutral): “Besides crowdsourcing, some datasets are collected from examinations designed by educational experts (Penas et al., 2014; Shibuki et al., 2014; Tseng et al., 2016; Clark et al., 2016; Lai et al., 2017), which aim to test human examinees.”
Improving Machine Reading Comprehension with General Reading Strategies (2018, methodology): “Besides crowdsourcing, datasets such as RACE (Lai et al., 2017) and ARC (Clark et al., 2018) are collected from language or science exams designed by educational experts (Penas et al., 2014; Shibuki et al., 2014; Tseng et al., 2016) to evaluate the comprehension level of human participants.”
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning (2020, methodology): “Besides, NTCIR QA Lab (Shibuki et al., 2014) offers comparative evaluation for solving real-world university entrance exam questions; The dataset of CLEF QA Entrance Exams Task (Rodrigo et al., 2015) is extracted from standardized English examinations for university admission in Japan; ARC dataset (”
Evidence Extraction for Machine Reading Comprehension with Deep Probabilistic Logic (2018, background): “Instead, researchers also collected multiple-choice datasets (Penas et al., 2014; Clark et al., 2016; Lai et al., 2017) from exams, which are designed to evaluate human participants.”
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants (2023, background): “CLEF Entrance Exams (Peñas et al., 2014) are large fitting datasets made from exam questions for English learners.”