SOTAVerified

Reading Comprehension

Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.

Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.

Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.

The Machine Reading group at UCL also provides an overview of reading comprehension tasks.

Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets

Papers

Showing 376400 of 1760 papers

TitleStatusHype
CoMiC: Adapting a Short Answer Assessment System for Answer Selection0
A Survey of Machine Narrative Reading Comprehension Assessments0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension0
Complex Factoid Question Answering with a Free-Text Knowledge Graph0
Complex Reading Comprehension Through Question Decomposition0
Complex Word Identification Based on Frequency in a Learner Corpus0
Composing Answer from Multi-spans for Reading Comprehension0
CoMeT: Integrating different levels of linguistic modeling for meaning assessment0
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets0
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension0
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension0
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation0
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements0
Computational Approaches to Sentence Completion0
Computing Semantic Text Similarity Using Rich Features0
Deep Understanding based Multi-Document Machine Reading Comprehension0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks0
DEIM: An effective deep encoding and interaction model for sentence matching0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning0
Combining Formal and Distributional Models of Temporal and Intensional Semantics0
Constructing Datasets for Multi-hop Reading Comprehension Across Documents0
Assessing Distractors in Multiple-Choice Tests0
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop0
Assessing Conformance of Manually Simplified Corpora with User Requirements: the Case of Autistic Readers0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
5Knowledge modelTest79.2Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2Unverified
7LReasoner ensembleTest76.1Unverified
8ELECTRA and ALBERTTest71Unverified
9WWZTest69.7Unverified
10xlnet-large-uncased [extended data]Test69.3Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4Unverified
2Megatron-BERT (ensemble)Accuracy90.9Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8Unverified
4Megatron-BERTAccuracy89.5Unverified
5XLNetAccuracy (Middle)88.6Unverified
6DeBERTalargeAccuracy86.8Unverified
7B10-10-10Accuracy85.7Unverified
8RoBERTaAccuracy83.2Unverified
9Orca 2-13BAccuracy82.87Unverified
10Orca 2-7BAccuracy80.79Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94Unverified
2MT5 LargeAverage F10.84Unverified
3ruRoberta-large finetuneAverage F10.83Unverified
4ruT5-large-finetuneAverage F10.82Unverified
5Human BenchmarkAverage F10.81Unverified
6ruT5-base-finetuneAverage F10.77Unverified
7ruBert-large finetuneAverage F10.76Unverified
8ruBert-base finetuneAverage F10.74Unverified
9RuGPT3XL few-shotAverage F10.74Unverified
10RuGPT3LargeAverage F10.73Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4Unverified
2BERT-LargeOverall: F162.7Unverified
3BiDAFOverall: F128.5Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55Unverified