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 651700 of 1760 papers

TitleStatusHype
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension0
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents0
Scoping natural language processing in Indonesian and Malay for education applications0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension0
A Survey of Machine Narrative Reading Comprehension Assessments0
OPERA:Operation-Pivoted Discrete Reasoning over Text0
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source0
TangoBERT: Reducing Inference Cost by using Cascaded Architecture0
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State TrackingCode0
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
Improving Zero-Shot Event Extraction via Sentence Simplification0
Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask0
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension0
Analyzing Wrap-Up Effects through an Information-Theoretic Lens0
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension0
Lite Unified Modeling for Discriminative Reading ComprehensionCode0
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension0
DEIM: An effective deep encoding and interaction model for sentence matching0
Calibration of Machine Reading Systems at Scale0
Information retrieval for label noise document ranking by bag sampling and group-wise loss0
What Makes Reading Comprehension Questions Difficult?Code0
Feeding What You Need by Understanding What You Learned0
Read before Generate! Faithful Long Form Question Answering with Machine Reading0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
Deep Understanding based Multi-Document Machine Reading Comprehension0
Using calibrator to improve robustness in Machine Reading Comprehension0
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words0
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models0
PQuAD: A Persian Question Answering Dataset0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?0
An MRC Framework for Semantic Role Labeling0
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking0
Data Augmentation for Biomedical Factoid Question Answering0
Cooperative Self-training of Machine Reading Comprehension0
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding0
CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering0
Event Detection via Derangement Reading Comprehension0
JEFF - Just Another EFFicient Reading Comprehension Test Generation0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension0
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension0
Roof-BERT: Divide Understanding Labour and Join in Work0
ChartText: Linking Text with Charts in Documents0
Multi Document Reading Comprehension0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management0
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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