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 201250 of 1760 papers

TitleStatusHype
NarrativeXL: A Large-scale Dataset For Long-Term Memory ModelsCode1
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named EntitiesCode1
NorQuAD: Norwegian Question Answering DatasetCode1
NT5?! Training T5 to Perform Numerical ReasoningCode1
Open-Domain Question Answering Goes Conversational via Question RewritingCode1
Open-Retrieval Conversational Machine ReadingCode1
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention NetworksCode1
Parallel Instance Query Network for Named Entity RecognitionCode1
PMG : Personalized Multimodal Generation with Large Language ModelsCode1
PolicyQA: A Reading Comprehension Dataset for Privacy PoliciesCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
Clinical Reading Comprehension: A Thorough Analysis of the emrQA DatasetCode1
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningCode1
QANet: Combining Local Convolution with Global Self-Attention for Reading ComprehensionCode1
Adversarial Training for Commonsense InferenceCode1
Reading Wikipedia to Answer Open-Domain QuestionsCode1
ReClor: A Reading Comprehension Dataset Requiring Logical ReasoningCode1
Automated Scoring for Reading Comprehension via In-context BERT TuningCode1
Recurrent Chunking Mechanisms for Long-Text Machine Reading ComprehensionCode1
Relational Surrogate Loss LearningCode1
Retrospective Reader for Machine Reading ComprehensionCode1
Revealing the Importance of Semantic Retrieval for Machine Reading at ScaleCode1
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question DecompositionCode1
Benchmarking: Past, Present and FutureCode1
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading ComprehensionCode1
SemEval-2021 Task 4: Reading Comprehension of Abstract MeaningCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
Single-dataset Experts for Multi-dataset Question AnsweringCode1
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening ComprehensionCode1
SQuAD: 100,000+ Questions for Machine Comprehension of TextCode1
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question AnsweringCode1
Teaching Machine Comprehension with Compositional ExplanationsCode1
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional AnswersCode1
Densely Connected Attention Propagation for Reading ComprehensionCode1
TIE: Topological Information Enhanced Structural Reading Comprehension on Web PagesCode1
Token-Level Adaptation of LoRA Adapters for Downstream Task GeneralizationCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Training Language Models to Win Debates with Self-Play Improves Judge AccuracyCode1
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language ModelsCode1
Can large language models reason about medical questions?Code1
VisualMRC: Machine Reading Comprehension on Document ImagesCode1
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test ConstructionCode1
Towards artificial general intelligence via a multimodal foundation modelCode1
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement UnderstandingCode1
Annotating the MASC Corpus with BabelNet0
Annotating Story Timelines as Temporal Dependency Structures0
AgentInstruct: Toward Generative Teaching with Agentic Flows0
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications0
A Framework for Rationale Extraction for Deep QA models0
Benben: A Chinese Intelligent Conversational Robot0
Show:102550
← PrevPage 5 of 36Next →

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