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 14011450 of 1760 papers

TitleStatusHype
MMM: Multi-stage Multi-task Learning for Multi-choice Reading ComprehensionCode0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading ComprehensionCode0
SQL Generation via Machine Reading ComprehensionCode0
FastFusionNet: New State-of-the-Art for DAWNBench SQuADCode0
FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced LanguagesCode0
Automatically generating question-answer pairs for assessing basic reading comprehension in SwedishCode0
FactGuard: Leveraging Multi-Agent Systems to Generate Answerable and Unanswerable Questions for Enhanced Long-Context LLM ExtractionCode0
FusionNet: Fusing via Fully-Aware Attention with Application to Machine ComprehensionCode0
Visuo-Linguistic Question Answering (VLQA) ChallengeCode0
NaijaRC: A Multi-choice Reading Comprehension Dataset for Nigerian LanguagesCode0
Gated-Attention Readers for Text ComprehensionCode0
Gated End-to-End Memory NetworksCode0
Reasoning about Actions and State Changes by Injecting Commonsense KnowledgeCode0
World Knowledge in Multiple Choice Reading ComprehensionCode0
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and ModelsCode0
A Framework for Evaluation of Machine Reading Comprehension Gold StandardsCode0
Towards Interpreting BERT for Reading Comprehension Based QACode0
Automated Focused Feedback Generation for Scientific Writing AssistanceCode0
Extract, Integrate, Compete: Towards Verification Style Reading ComprehensionCode0
Discourse Comprehension: A Question Answering Framework to Represent Sentence ConnectionsCode0
Generating Question-Answer HierarchiesCode0
Discourse-Aware Semantic Self-Attention for Narrative Reading ComprehensionCode0
ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word PredictionCode0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case StudyCode0
mPMR: A Multilingual Pre-trained Machine Reader at ScaleCode0
GENIE: Toward Reproducible and Standardized Human Evaluation for Text GenerationCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
Dict2vec : Learning Word Embeddings using Lexical DictionariesCode0
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question AnsweringCode0
Unused information in token probability distribution of generative LLM: improving LLM reading comprehension through calculation of expected valuesCode0
MRCBert: A Machine Reading ComprehensionApproach for Unsupervised SummarizationCode0
MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension BenchmarkCode0
Augmenting Neural Networks with First-order LogicCode0
GMAT: Global Memory Augmentation for TransformersCode0
Stochastic Answer Networks for Machine Reading ComprehensionCode0
Capturing Greater Context for Question GenerationCode0
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading ComprehensionCode0
MRQA 2019 Shared Task: Evaluating Generalization in Reading ComprehensionCode0
Dice Loss for Data-imbalanced NLP TasksCode0
Diagnosing Medical Datasets with Training DynamicsCode0
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine ComprehensionCode0
Denoising Distantly Supervised Open-Domain Question AnsweringCode0
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?Code0
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understandingCode0
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehensionCode0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
Show:102550
← PrevPage 29 of 36Next →

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