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 13011350 of 1760 papers

TitleStatusHype
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network0
Machine Reading Comprehension using Case-based Reasoning0
Machine Reading Comprehension with Enhanced Linguistic Verifiers0
Machine Reading, Fast and Slow: When Do Models "Understand" Language?0
Machine Reading, Fast and Slow: When Do Models “Understand” Language?0
Machine Reading of Hypotheses for Organizational Research Reviews and Pre-trained Models via R Shiny App for Non-Programmers0
Machine Reading with Background Knowledge0
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models0
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition0
Making Neural Machine Reading Comprehension Faster0
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension0
Mapping Verbs in Different Languages to Knowledge Base Relations using Web Text as Interlingua0
MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions0
MC\^2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension0
MCDTB: A Macro-level Chinese Discourse TreeBank0
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension0
MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants0
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge0
MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text0
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals0
Measuring Frame Instance Relatedness0
Measuring text readability with machine comprehension: a pilot study0
Medical Exam Question Answering with Large-scale Reading Comprehension0
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension0
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller0
Meta Answering for Machine Reading0
Meta Sequence Learning for Generating Adequate Question-Answer Pairs0
面向垂直领域的阅读理解数据增强方法(Method for reading comprehension data enhancement in vertical field)0
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)0
Mimir: Improving Video Diffusion Models for Precise Text Understanding0
Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker0
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification0
MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge0
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition0
Modeling Biological Processes for Reading Comprehension0
Modeling Context in Answer Sentence Selection Systems on a Latency Budget0
Modeling Language Proficiency Using Implicit Feedback0
Modeling Task Effects in Human Reading with Neural Network-based Attention0
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension0
Modeling the Impact of Syntactic Distance and Surprisal on Cross-Slavic Text Comprehension0
Models can use keywords to answer questions that human cannot0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts0
Modular Self-Supervision for Document-Level Relation Extraction0
More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering0
MOTIF: Contextualized Images for Complex Words to Improve Human Reading0
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training0
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training0
MRCLens: an MRC Dataset Bias Detection Toolkit0
MRCLens: an MRC Dataset Bias Detection Toolkit0
Knowledgeable Dialogue Reading Comprehension on Key Turns0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
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